Indian Patents. 229509:DEVICE AND METHOD FOR MONITORING A TECHNICAL FACILITY COMPRISING MULTIPLE SYSTEMS, IN PARTICULAR A POWER PLANT FACILITY

Title of Invention	DEVICE AND METHOD FOR MONITORING A TECHNICAL FACILITY COMPRISING MULTIPLE SYSTEMS, IN PARTICULAR A POWER PLANT FACILITY
Abstract	The invention concerns a method and a corresponding device (1) for monitoring a technical installation (2). The invention is characterized in that a dynamic model (15) of at least one system (3,5,7,9, 11) of the technical installation is enhanced by means of an artificial intelligence based algorithm (21 21a, 21b) during the operation of said system (3,5,7, 9, 11).

Full Text	Description Device and method for monitoring a technical facility comprising multiple systems, in particular a power plant facility. The invention relates to a device as well as a method for monitoring a technical facility comprising multiple systems, in particular a power plant facility. Conventional devices and. methods for monitoring a technical facility comprising multiple systems, in particular diagnostic methods and diagnostic equipment, are often based on the observation and/or measurement of specific operational parameters of the technical facility, whereby exceeding or falling short of a reference value calls for a maintenance procedure. Naturally, the derivation of a necessary operational procedure by observing parameters measured in isolation is, at the same time, imprecise and prone to errors. If, on the other hand, an abundance of data that accumulates in the technical facility, in particular measurement values from various measurement positions and/or corresponding stored historical measurement values, is consulted in order to create a picture of the present or future expected operational status, then this likewise Leads to no satisfactory conclusion, because the mutual dependencies of this data from data sources which are gene rally highly diverse are mostly unknown, and therefore, likewise, no precise evaluation or even prediction of the operational situation from it is possible. In addition, it is to be expected that not all data that exerts an influence on the operational situation of the facility is included, which makes the problem even more complicated. As a result, the object of the invention is to show an improved device as well as a method for monitoring a technical facility comprising multiple systems, in particular a power plant facility. At the same time, high prediction accuracy in particular should be achievable with regard to a developing failure in the technical facility. In addition, so-called "creeping process deviations" that lead away from a desired operational situation and practically always precede the appearance of a failure and/or a process disruption should be able to be identified as early in time as possible. Furthermore, the expected point in time of the appearance of a failure should be identifiable as early in time as possible by means of a device according to the invention or a corresponding method, such nhat countermeasures (for example, a maintenance procedure) can be initiated before a failure of the facility or its components occurs. In addition, a device according to the invention as well as a corresponding method should reduce the expense of diagnostic applications in the facility that have been customary up until now, and furthermore allow a better optimization of the control devices used. The object according to the invention with regard to the device is achieved by means of a device for monitoring a technical facility comprising multiple systems, in particular a power plant facility, incLuding • at least one analysis module, which includes a dynamic model of at least one system of the technical facility, whereby operational and/or structural data for the technical facility can be supplied to the analysis module as input data, and • at least one algorithm based on artificial intelligence incorporated by the analysis module, by means of which the dynamic model of the system can be improved during the operation of the system, whereby output data is identifiable by means of the analysis module, which characterizes the present and/or future operational behavior of the system. At the same time, the invention starts from the consideration that, with conventional modeling known from the prior art, the achievable precision and thus the achievable degree of agreement with the corresponding actual measurements for the identified model measurements is too limited to come to reliable conclusions about a future behavior of the facility. At the present time, known modelings offer the most suitable results, i.e. there exists a high degree of agreement with the corresponding actual measurements at: the present time. Therefore, the further in the future that the relevant point in time for the behavior of :he facility lies, the greater the unreliability of the predict--on. An additional starting point for the: invention lies in the realization that, in many cases, it is impossible or possible only at extremely great expense to specify a fairly accurate model for the technical facility (for example, because of a strongly non-linear behavior of certain systems of the technical. facility) . With the device according to the invention, a dynamic model of at least one system of the technical facility is assumed, which is improved during operation by means of methods of artificial intelligence. The capability of the analysis module to describe and to forecast the operational behavior of the system is thereby improved. At the same time, it is not urgently necessary to start with a complex, uniform dynamic model of the system. For example, a set of a few insular, simple equations and characteristics, which can be supplemented by means of a neural network (preferably structured in a simple manner), fuzzy logic or a genetic algorithm, often suffices. The interaction between these "partial models" for a system description is then improved during operation by means of the algorithm based on artificial intelligence such that an interrelationship develops for the said elements. A model, particularly a deterministic one in the classical sense, is not necessary. On the contrary, the mentioned interrelationship is parameterized (for example, a Bernoulli equation for this relationship in order to use it on a specific existing flow), and the algorithm based on artificial intelligence searches in historical or present operational data and/or structural data of the system and/or of the technical facility for correlations, for example changes in measurements that are created as a consequence of the change in other measurements. Newly-discovered correlations of this type are then integrated into the dynamic model by means of the algorithm based on artificial intelligence - in particular as an additional characteristic and/or equation or as an adjustment of parameters oi the dynamic model, for example of the network weight factors of a neural network - and these are thereby improved. In the context of the invention, the term "system" should cover the range from a simple component - for example, a pipeline - up to a highly complex complete system, including a number of subsystems - for example a turbine set, a boiler facility, a power plant block or the complete power plant. The term "operational data" is to be understood to mean, in particular, all types of data that accrue during the operation of the technical facility such as, for example, temperature measurements, pressure measurement data, thermal images, sensor data, messages, alarms, warning, and so forth. The "algorithm based on artificial intelligence" includes, in particular, methods of artificial intelligence such as neural networks, fuzzy logic and genetic atlgorithms. The "dynamic model" can be described deterministically and numerically or also by means of methods based on artificial intelligence. Furthermore, at can include physical and mathematical equations. Combinations of the mentioned elements are also included, in particular physical and/or mathematical equations that are linked by means of methods based on artificial intelligence. In a preferred embodiment, the improvement of the dynamic model includes the identification of that input data which has not yet been previously used by the dynamic model, and the dynamic model can be expanded with the help of this input data. At the same time, the algorithm based on artificial intelligence is used for the improvement of the dynamic model for the identification and establishment of correlations not yet considered in the dynamic model. The dynamic model preferably includes one or more elements from the group {characteristic, physical equation, neural network, fuzzy logic, genetic algorithm}. The dynamic model particularly includes at least one neural network, which can be trained with historical operational data from the system. The modeling of technical components and facilities by means of neural networks is a known and proven procedure. A particular advantage to be seen therein is that an analytical description of the components to be modeled heed not be known. The structure of the neural network first initialized by means of initial parameters ("initial weighting factors") and determined in advance througn the training phase (which, for example, includes a known backpropagation algorithm) is designed with respect to its weighting factors such that a good correlation with the actual component can be expected after the conclusion of the training phase. In this manner one obtains a model of the component without being required to undertake a precise analytical analysis. In the training phase, the neural network learns to respond to specific input values with specific output values; together with their corresponding output values, input values of this type are often designated as a training set. In operation, the neural network then interpolates for input values that are not included in the training set, such that output values are also calculated for input values of this type. During operation of the technical facility, the problem often appears that not all operational data that exerts an influence on the behavior of the component(s) to be modeled (or the entire technical facility as well) are known or ascertainable. Furthermore, the use of at Least one algorithm based on artificial intelligence makes it possible, by means of the dynamic model, to incorporate into the calculations of the status of the system of the technical facility those parameters that do not act directly on this system of the technical facility, for example as input and/or output signals or media flows. For example, for a serially ordered chain of systems, the modeling of a system located in the middle of this chain is provided, which - alongside input signals where appropriate acting directly upon this system - derive input signals from the preceding system that are not measurable or available in other ways. At the same time, the methods of artificial intelligence (which can be designed according to biological evolution, for example, as genetic search algorithms based on a suitable characteristic combination) a Lso them allow a calculation of the status of a system of the technical facility if the input parameters for the determination of the current status are largely unknown or identifiable only with difficulty, for example - as mentioned before - by means of a complex measurement of the output values of the preceding system. For example, statistical methods can also be used at the same time in connection with the algorithm based on artificial intelligence, whereby the most probable input and/or output values for a system that are not otherwise accessible are used .in a current operational situation in which the algorithm based on artificial intelligence determines these input and/or output values of the respective system which are required by the dynamic model, for example, through an evolutionary search strategy. In this manner, a good correlation for the model of at least one system can be expected with the actual behavior of this system, because also included in the modeling of the system by means of at least one algorithm based on artificial intelligence is that operating data that would otherwise be ignored and would lead to a more or less high level of imprecision for the model and, for this reason especially, the predictions created with it. Input and/or output data for the system that determines its operational status, but is not accessible, for example, by means of measurement, is also included for this reason in particular. Thus, the precision of the prediction is increased. A particularly preferred embodiment of the invention consists of a number of analysis modules, which each include a dynamic model of at least one system of the technical facility. Furthermore, at least one additional algorithm based on artificial intelligence is provided at the same time, by means of which correlations at least between the input and/or output data of a first of the analysis modules and the input and/or output data of a second of the analysis modules is identifiable. This embodiment of the invention relates to the expansion of the device according to the Invention to parallel monitoring of interacting systems, whereby the interaction in the form of a relationship between the respective input and/or output data of the analysis modules is determined from additional algorithms based on artificial intelligence and is established as additional correlations (for example, in the form of an equation, a neural network or a characteristic). Thus develops a precise dynamic model of the interactive systems comprising the dynamic model of the individual systems as well as the additional correlations. Thus, the current and/or future operational behavior of the individual systems as well as the operational behavior of the facility resulting from the interaction of the systems can be described. Advantageously identifiable at the same time by means of the correlations is additional cutput data that characterizes the current and/or future operational behavior of the technical facility, whereby this additional output data includes cross- system information. Correlations between the said data indicate mutual dependencies, by which the additional output data thus extracted goes beyond the system limits of the individual systems concerned in its informative value, and thus describes the behavior of a larger unit of the technical facility consisting of at least two systems. Preferably, the operational and/or structural data of the technical facility includes cne or more items of information from the group {process data, operational messages, warning messages, disruption messages, monitoring notifications, comments, design of the technical facility, hierarchy of the facility components}. At the same time, the process data can be acquired online and offline from a control system of the technical facility and/or a subsystem associated with it, or also manually entered. The operational messages particularly include sensor data and information derived therefrom about the operational status of the technical facility and its systems. The structural data particularly includes information about the design of the technical facility with regard to the systems comprising the technical facility (facility components, subsystems, system groups) as well as their hierarchical interaction and prioritization. At the same time, this data can include current and/or historical data, which, for example, is recorded in a short- or long-term archive or in an engineering system. The operational and/or structural data is preferably supplied by a process control system. For operating and monitoring complex technical facilities, a process control system in which the mentioned data is available or accrues during operation and is stored is typically used. In this embodiment, the data provision is therefore of especially low complexity. Furthermore, the invention leads to a method for monitoring a technical facility comprising multiple systems, particularly a power plant facility, including the following steps: • Operational and/or structural data from the technical facility is provided as input data to a dynamic model of at least one system of the technical facility, • the dynamic model of the system is improved during the operation of the system by means of an algorithm based on artificial intelligence, and • output data that characterizes the current and/or future operational behavior of the system is determined by means oi the dynamic model. The improvement of the dynamic model preferably includes the identification of that input data that has not yet been previously used by the dynamic model, and the dynamic model can be expanded with the help of this input data. In a further embodiment, a number of dynamic models are provided that in each case describe at least one system of the technical facility and at least one additional algorithm based on artificial intelligence, by means of which correlations at least between the input and/or output data of a first of the dynamic models and the input and/or output data of a second of the dynamic models are identifiable. Advantageously identifiable by means of the correlations is additional output data that characterizes the current and/or future operational behavior of the technical facility, whereby this additional output data includes cross-system information. The explanations provided in connection with the device according to the invention and its advantageous embodiments are transferable to the method according to the invention and are therefore not repeated here. In summary, the invention can be incorporated into the following environment: Artificial intelligence fcr the diagnosis of systems of a technical facility, for example a power plant facility, can be used to predict foreseeable failures, whereby all data available in the technical facility can be consulted. At the same time, the main points of emphasis lie, for example, in genetic algorithms and neural networks for modeling and accomplishing the monitoring tasks, particularly diagnosis tasks. It is of particular interest to decidedly reduce the expense of diagnosis applications in the technical facility, and furthermore to make possible an improved optimization of the controls. An improvement is achieved of, on the one hand, the relevant aggregate characteristics of systems of the technical facility, for example, power and energy consumption, are reduced with regard to legal regulations and resource scarcity. On the other hand, customer wishes for improved performance and diagnosis facilities are fulfilled. Both large systems and small systems can be integrated into the diagnosis by means of genetic/evolutionary algorithms. It is possible to facilitate conclusions about the status of at least one system of the technical facility by associating genetic (evolutionary) algorithms with Kohonen networks and/or neural networks of any type. The use of genetic algorithms therefore also makes it possible to include in the determination of the status of a system of the technical facility those parameters that do not directly affect this component of the technical facility, for example as input and/or output signals or media flows. The methodologies of genetic algorithms (search algorithms) therefore also then allow a calculation of the status of at least one system or of the entire technical facility if the input parameters for the determination of the current status are largely unknown and/or not identifiable or identifiable only with difficulty, for example, by means of an expensive measurement. Furthermore, the use of artificial intelligence for the diagnosis makes it possible for deviations from calculated current statuses to be reported to the operator of the technical facility in the case of complex facility statuses. A specific failure notice, for example about the narrowly isolated failure location, can be initially dispensed with in this case, because failures, for example of sensors, are usually measured and reported in any event by an existing control system. What is instead important in connection with the invention is the identification of creeping processes - that do not necessarily cause immediate failure of a facility component - such as contamination, loss of power through wear, ageing and so forth, which are incorrectly perceived or incorrectly interpreted by people because; of the "habituation effect". In many cases, creeping changes of this type sometimes lead to the failure of the technical facility. The changes are, however, often not identified because an existing control device, for example, attempts to counter this change. Contamination on the blades of a fan is compensated for by re- adjusting the fan blades, for example. Or the control device compensates for decreased outputs in oil pumps or coolant pumps through new reference value specifications; then the temperature of a bearing, for example, becomes higher only very slowly, because the control device can often delay the moment of system failure in the case of an impending failure. At the same time, however, ever more is demanded of the controlled systems, and the wear increases. The user of the technical facility notices nothing as a result, because, according to the control device, the technical facility continues to function even though one or more systems of the technical facility get closer to their wear limit. A risky operation exists in particular if a functioning system is operated under increased stress; such stress can be generated through a control device mentioned previously by means of a reference value specification. For example, a coolant loop is constructed for continuous operation at 50% output. Permanent operation at 70-80% output can then soon lead to serious damages. An impending failure of a coolant pump stricken with a leak remains unnoticed, however, because the control device increases the reference value (i.e. the pressure) for the coolant pump more and more to maintain the function of the coolant loop, which accelerates the failure of the pump further. Not until the failure actually occurs is the failure of the coolant pump noticed as the source of the failure of the cooling system. A device according to the invention as well as a method can provide a remedy here. Furthermore, genetic algorithms in conjunction with intelligent, adaptive networks allow the identification of risky operating modes of the technical facility, capacity overload or incorrect capacity utilization of units and systems, and so forth. This is advantageously reported to the operator/user of the technical facility, for example in the form of an operational diagram (i.e. a characteristic diagram) , from which results the current operation as well as a proposed improved operation. The presentation of deviations can occur advantageously by means of characteristic diagrams. In addition to the failure prediction, an optimization of the operation of the operation of the technical facility is also possible based on genetic algorithms. Furthermore, information can be extracted by means of genetic algorithms for the management personnel of the technical facility, which makes possibLe a conclusion about the overall status of the facility and if appropriate about maintenance procedures necessary within a time interval. The use of artificial intelligence advantageously makes possible an online calculation of system statuses, i.e. the operator can be notified of a. "failure behavior" in his facility and is then able to make anticipatory calculations that make a new approach possible for him. Example: A device according to the invention, for example in the form of a diagnostic system, reports "Failure in coal pulverizer XX grinding rollers zone" to the operator; it is determined through counterchecks that a maintenance procedure for the coal pulverizer is necessary (for example, because this is prescribed by the manufacturer in the related maintenance manual) . By means of anticipatory calculation, it can be determined by means of the diagnostic system according to the invention what would occur if the operator nevertheless leaves his technical facility in operation without maintenance procedures and when the actual appearance of an operational failure of the coal pulverizer is to be expected. With the combination of genetic algorithms and neural networks as well as optional Kohonen networks, a multitude of conclusions can be reached with respect to the current and/or future status of the technical facility, in particular when a maintenance procedure will oe necessary. In the following two exemplary embodiments of the invention are described in more detail.. They show: FIG 1 a system hierarchy, as customarily occurs in technical facilities, FIG 2 a device according to the invention, and FIG 3 a further embodiment c f a device according to the invention with two analysis modules. FIG 1 shows by way of example a hierarchical system design of a technical facility 2. The technical facility 2 is designed as a power plant facility for the generation of electrical energy and includes two power plant blocks 3. Each power plant block 3 includes two turbines 5, for example gas turbines. These turbines 5 in turn each contain a coolant loop 9. This coolant loop 9 includes a turbine blade 11 of the turbine 5. Each of the mentioned elements should fall under the term system in connection with the invention. A system can therefore include a simple, isolated component such as, for example a turbine blade, as well as a complex system, such as the power plant block 3 or multiple power plant blocks 3. FIG 2 shows a device 1 according to the invention with an analysis module 13. At the same time, operational data 17 and structural data 19 from the technical facility is conveyed to the analysis module 13 as input data. The operational data 17 can for example involve online measurement data which is recorded in the technical facility in the system itself by means of sensors. At the same time, it can also involve data derived from this measurement data, which is produced in a computer system, for example. Furthermore, the operationa] data 17 can also include offline measurement data, which is stored in an archive or manually entered, for example. The structural data 19 describes the technical facility or the system itself. In particular, it includes information about the interconnection of subsystems that are included in the system and their hierarchical arrangement. A dynamic model 15 is proviced for modeling the system behavior. This model 15 can include analytic equations, for example, as well as methods of artificial intelligence such as, for example, neural networks, fuzzy logic or genetic algorithms. Furthermore, simple characteristics can in particular be provided for the description of the system behavior. An algorithm 21 based on artificial intelligence is provided for the improvement of the dynamic model 15 during the operation of the system 15. This algorithm 21 based on artificial intelligence can be designed as a genetic algorithm, for example. An important role for this algorithm 21 consists in effecting dynamic adjustments in the model 15 in order to achieve an improvement of this model 15 in the sense that an improved model behavior and thus a better correlation with the behavior of the actual system is achieved. For example, a modeling error can be called upon for the evaluation of this circumstance, for example, the difference between the actual chronological behavior of the system and the modeled chronological behavior of this system. An improvement of the model 15 can then take place by means of the algorithm 21 based on artificial intelligence. At the same time, the algorithm 21 based on artificial intelligence is particularly used to identify parameters and data not yet considered during modeling which are included in the operational data 10 and/or the structural data 19 but have not yet been called upon for modeling, and to establish further correlations, for example equations or characteristics, including the mentioned identified parameters and/or data, and to add them to the dynamic model 15. An algorithm 21 based on artificial intelligence designed as a genetic algorithm optimizes correlations included in the dynamic model 15 such as, for example, equations, characteristics or network parameters of a neural network, in that it combines and re-conbines evolutionary parameters and data and, at the same time, discovers new correlations in particular, which are not yet included in the dynamic model 15. In this respect, the described modeling used in connection with the invention and its improvement by means of the algorithm 21 based on artificial intelligence goes beyond known methods of, for exampLe, supervised learning and classical modeling. The analysis model 13 produces conclusions about the operational behavior of the system as output data 23. At the same time, for example, it can involve current or future operational behavior of the system (creation of a prediction). For example, operational data 17 is conveyed to the analysis module 13, and it is assumed that this operational data will persist over a particular future time period. The output data 23 then allows a conclusion, for example, as to whether and, as the case may be, when a disruption of the system's operation is to be expected. The more precisely the model 15 reflects the actual system behavior, the more precise is this conclusion. A high level of precision for the model 15 is provided by device 1 according to the invention, in particular by means of the algorithm 21 based on artificial intelligence, such that the predictions and diagnoses determined as output data 23 by the analysis module 13 are very precise. The output data 23 includes, in particular, qualified reports with regard to failure identification (trend analysis, wear and ageing), efficiency, process quality and expected future behavior of the system and the technical facility. In order to produce reports of this type, a set of rules can be included in analysis module 13 in order to transform output data generated from the model 15 into the mentioned reports. At the same time, the set of rules can include rules for the prediction of a short-tern observation period in particular as well as rules for a long-term observation period. At the same time, in addition to the output data from the model 15, additional information can be conveyed to the set of rules, for example reports and alarms related to the system or the technical facility. In the illustration for FIG 3, a device 1 according to the invention includes two analysis modules 13a and 13b. At the same time, operational data 17a and structural data 19a from a coolant system 29 is conveyed to the analysis module 13a; the analysis module 13b receives operational data 17b and structural data 19b from a generator 31 as input data. In addition, environmental data 33 for the technical facility is conveyed to both analysis modules 13a, 13b, for example the ambient temperature, atmospheric humidity, atmospheric pressure and so forth. Each analysis module 13a, 13b detects output data 23a or 2 3b, which characterizes the operational behavior of the respective analyzed system 29 or 31. Because the coolant system 29 and the generator 31 are to be considered systems not procedurally isolated from one another, it is to be anticipated that changing operational data 17a from the coolant system 29 in particular influences the system behavior of the generator 31, and thus the output data 23b from the analysis module 13b. The same applies for changing operational data 17b from the generator 31, from which it can be expected that the operational behavior of the coolant system 2 9 and thus the output data 23a from the analysis module 13a changes as a result. In order to detect and quantify correlations of this type, the additional algorithm 25 based on artificial intelligence is provided. This can be designed, for example, as an additional genetic algorithm that produces additional output data 27, which includes cross-system information, and thus goes beyond the characterization of the behavior of one of the systems, and in particular contains information about the interaction of the systems 29 and 31 and their mutual dependencies. At the same time, the additional algorithm 25 based on artificial intelligence is therefore responsible for identifying and establishing higher-level cross-system correlations. These correlations can include, for example, equations, characteristics or neural networks, which are produced and/or parameterized by the additional algorithm 25 based on artificial intelligence. At the same time, the strategy for the identification and establishment of cross-system correlations of this type can be similar to the identification and establishment of additional internal system correlations mentioned in connection with FIG 2 by means of the algorithms 21a, 21b based on artificial intelligence. By using a device according to the invention and a method according to the invention, it should be possible, in particular, to create conclusions about the system behavior, in particular about that ir. the future, from a system's existing operational and structural data without complex diagnostic instruments. A self-adaptive dynamic model of the system is provided for this purpose, which is improved during operation by an algorithm based on artificial intelligence. The algorithm 21 based on artificial intelligence is used in particular for searching for correlations in a technical facility's operational and/or structural data that is typically available and processed in a control system, for example, and for integrating into the dynamic model the correlations identified in doing so in order to improve the said model incrementally. It is therefore not necessary that an analytical model of the system or of the technical .facility exists. Instead, the model based on, for example, a very simple characteristic from a characteristic array and/or on simple equations, is improved incrementally by means of a correlation analysis of the operational and structural data by means of the algorithm based on artificial intelligence by establishing the correlations determined in coing so, for example, in the form of additional characteristics, equations, and so forth. In contrast to conventional monitoring and diagnostic devices, the present is preferably based on a data-based method, whereby dependencies between parts of existing operational data and/or between parts of structural data from a technical facility are detected using methods of artificial intelligence and established as quantified correlations, for example equations and/or characteristics, such that a precise dynamic model of at least one system of the technical facility is produced. Claims 1. Device (1) for monitoring a technical facility (2)' comprising multiple systems, in particular a power plant facility, characterized by • at least one analysis module (13, 13a, 13b), which includes a dynamic model (15) of at least one system (3, 5, 7, 9, 11) of the technical facility (2), whereby operational (17, 17a, 17b) and/or structural data (19, 19a, 19b) from the technical facility (1) can be conveyed to the analysis module (13, 13a, 13b) as input data, and • at least one algorithm (21, 21a, 21b) based on artificial intelligence included in the analysis module (13, 13a, 13b), by means of which the dynamic model (15) of the system (3, 5/ 7, 9, 11) can be improved during the operation of the system whereby output data (23, 23a, 23b) is identifiable by means of the analysis module (13, 13a, 13b) and characterizes the current and/or future operational behavior of the system (3, 5, 7, 9, 11) . 2. Device (1) according to Claim 1, characterized in that the improvement of the dynamic model (15) includes the identification of that input data that has not yet been previously used by the dynamic model (15), and in that the dynamic model (15) can be oxpand€;d with the help of this input data. 3. Device (1) according to Claim 1 or 2, in which the dynamic model (15) includes one or more elements from the group {characteristic, physical equation, neural network, fuzzy logic, genetic algorithm}. 4. Device (1) according to one of the Claims 1 to 3, whereby the dynamic model (15) includes at least one neural network, which can be trained using historical operational data from the system (3, 5, 7, 9, .1). 5. Device (1) according to one of the Claims 1 to 4, characterized in that a number of analysis modules (13, 13a, 13b) are available, which include in each case a dynamic model (15) of at least one system (3, 5, 7, 9, 11) of the technical facility (2), and in that at least one additional algorithm (25) based on artificial intelligence is provided, by means of which correlc tions at least between the input and/or output data of a first of the analysis modules (13, 13a, 13b) and the input and/or output data of a second of the analysis modules (13, 13a, 13b) are identifiable. 6. Device (1) according to Claim 5, characterized in that additional output data (27) is identifiable by means of the correlations, said data characterizing the current and/or future operational behavior of the technical facility (1) , whereby this additional cutput data (27) includes cross- system information. 7. Device (1) according to one of the Claims 1 to 6, whereby the operational data (17, 17a, 17b) and/or structural data (19, 19a, 19b) of the technical facility (2) includes one or more items of information from the group {process data, operational messages, warning messages, disruption messages, monitoring notifications, comments, design of the technical facility, hierarchy of the facility components}. 8. Device (1) according to o.ie of the claims 1 to 7, whereby the operational data (17, 17a, 17b) and/or structural data (19, 19a, 19b) of the technical facility (2) includes current and/or historical data from the technical facility (2) . 9. Device (1) according to one of the Claims 1 to 8, whereby the operational data (17,, 17a, 17b) and/or structural data (19, 19a, 19b) from the technical facility (2) is provided by a process control system of the technical facility (2). 10. Method for monitoring a technical facility (2) comprising multiple systems, in particular a power plant facility, characterized by the following steps: • Operational data (17, 17a, 17b) and/or structural data (19, 19a, 19b) from tie technical facility (2) is conveyed to a dynamic model at least of one system (3, 5, 7, 9, 11) of the technical facility (2) as input data, • the dynamic model (15) of the system (3, 5, 7, 9, 11) is improved during the operation of the system (3, 5, 7, 9, 11) by means of an algorithm (21, 21a, 21b) based on artificial intelligence, and • by means of the dynamic model (15) , output data (27) is identified which characterises the current and/or future operational behavior of the system (3, 5, 7, 9, 11). 11. Method according to Claim 10, characterized in that the improvement of the dynatmic model (15) includes the identification of that input data which has not yet been previously used by the dynamic model (15), and in that the dynamic model (15) can be expanded with the help of this input data. 12. Method according to one of the Claims 10 or 11, characterized in that a number of dynamic models (15) are provided, which in each case describe at least one system (3, 5, 7, 9, 11) of the technical facility, and that at least one additional algorithm (21, 21a, 21b) based on artificial intelligence is, provided, by means of which correlations at least between the input and/or output data of a first of the dynamic models (15) and the input and/or output data of a second of the dynamic models (15) are identifiable. 3. Method according to Claim 12, characterized in that additional output data is identifiable by means of the correlations, said data characterizing the current and/or future operational behavior of the technical facility (2), whereby this additional output data includes cross-system information. The invention concerns a method and a corresponding device (1) for monitoring a technical installation (2). The invention is characterized in that a dynamic model (15) of at least one system (3,5,7,9, 11) of the technical installation is enhanced by means of an artificial intelligence based algorithm (21 21a, 21b) during the operation of said system (3,5,7, 9, 11).

Full Text

Description
Device and method for monitoring a technical facility
comprising multiple systems, in particular a power plant
facility.
The invention relates to a device as well as a method for
monitoring a technical facility comprising multiple systems,
in particular a power plant facility.
Conventional devices and. methods for monitoring a technical
facility comprising multiple systems, in particular diagnostic
methods and diagnostic equipment, are often based on the
observation and/or measurement of specific operational
parameters of the technical facility, whereby exceeding or
falling short of a reference value calls for a maintenance
procedure.
Naturally, the derivation of a necessary operational procedure
by observing parameters measured in isolation is, at the same
time, imprecise and prone to errors.
If, on the other hand, an abundance of data that accumulates
in the technical facility, in particular measurement values
from various measurement positions and/or corresponding stored
historical measurement values, is consulted in order to create
a picture of the present or future expected operational
status, then this likewise Leads to no satisfactory
conclusion, because the mutual dependencies of this data from
data sources which are gene rally highly diverse are mostly
unknown, and therefore, likewise, no precise evaluation or
even prediction of the operational situation from it is
possible.
In addition, it is to be expected that not all data that
exerts an influence on the operational situation of the
facility is included, which makes the problem even more
complicated.
As a result, the object of the invention is to show an
improved device as well as a method for monitoring a technical
facility comprising multiple systems, in particular a power
plant facility. At the same time, high prediction accuracy in
particular should be achievable with regard to a developing
failure in the technical facility.
In addition, so-called "creeping process deviations" that lead
away from a desired operational situation and practically
always precede the appearance of a failure and/or a process
disruption should be able to be identified as early in time as
possible.
Furthermore, the expected point in time of the appearance of a
failure should be identifiable as early in time as possible by
means of a device according to the invention or a
corresponding method, such nhat countermeasures (for example,
a maintenance procedure) can be initiated before a failure of
the facility or its components occurs.
In addition, a device according to the invention as well as a
corresponding method should reduce the expense of diagnostic
applications in the facility that have been customary up until
now, and furthermore allow a better optimization of the
control devices used.
The object according to the invention with regard to the
device is achieved by means of a device for monitoring a
technical facility comprising multiple systems, in particular
a power plant facility, incLuding
• at least one analysis module, which includes a dynamic model
of at least one system of the technical facility, whereby
operational and/or structural data for the technical
facility can be supplied to the analysis module as input
data, and
• at least one algorithm based on artificial intelligence
incorporated by the analysis module, by means of which the
dynamic model of the system can be improved during the
operation of the system,
whereby output data is identifiable by means of the analysis
module, which characterizes the present and/or future
operational behavior of the system.
At the same time, the invention starts from the consideration
that, with conventional modeling known from the prior art, the
achievable precision and thus the achievable degree of
agreement with the corresponding actual measurements for the
identified model measurements is too limited to come to
reliable conclusions about a future behavior of the facility.
At the present time, known modelings offer the most suitable
results, i.e. there exists a high degree of agreement with the
corresponding actual measurements at: the present time.
Therefore, the further in the future that the relevant point
in time for the behavior of :he facility lies, the greater the
unreliability of the predict--on.
An additional starting point for the: invention lies in the
realization that, in many cases, it is impossible or possible
only at extremely great expense to specify a fairly accurate
model for the technical facility (for example, because of a
strongly non-linear behavior of certain systems of the
technical. facility) .
With the device according to the invention, a dynamic model of
at least one system of the technical facility is assumed,
which is improved during operation by means of methods of
artificial intelligence. The capability of the analysis module
to describe and to forecast the operational behavior of the
system is thereby improved.
At the same time, it is not urgently necessary to start with a
complex, uniform dynamic model of the system. For example, a
set of a few insular, simple equations and characteristics,
which can be supplemented by means of a neural network
(preferably structured in a simple manner), fuzzy logic or a
genetic algorithm, often suffices. The interaction between
these "partial models" for a system description is then
improved during operation by means of the algorithm based on
artificial intelligence such that an interrelationship
develops for the said elements.
A model, particularly a deterministic one in the classical
sense, is not necessary. On the contrary, the mentioned
interrelationship is parameterized (for example, a Bernoulli
equation for this relationship in order to use it on a
specific existing flow), and the algorithm based on artificial
intelligence searches in historical or present operational
data and/or structural data of the system and/or of the
technical facility for correlations, for example changes in
measurements that are created as a consequence of the change
in other measurements. Newly-discovered correlations of this
type are then integrated into the dynamic model by means of
the algorithm based on artificial intelligence - in particular
as an additional characteristic and/or equation or as an
adjustment of parameters oi the dynamic model, for example of
the network weight factors of a neural network - and these are
thereby improved.
In the context of the invention, the term "system" should
cover the range from a simple component - for example, a
pipeline - up to a highly complex complete system, including a
number of subsystems - for example a turbine set, a boiler
facility, a power plant block or the complete power plant.
The term "operational data" is to be understood to mean, in
particular, all types of data that accrue during the operation
of the technical facility such as, for example, temperature
measurements, pressure measurement data, thermal images,
sensor data, messages, alarms, warning, and so forth.
The "algorithm based on artificial intelligence" includes, in
particular, methods of artificial intelligence such as neural
networks, fuzzy logic and genetic atlgorithms.
The "dynamic model" can be described deterministically and
numerically or also by means of methods based on artificial
intelligence. Furthermore, at can include physical and
mathematical equations. Combinations of the mentioned elements
are also included, in particular physical and/or mathematical
equations that are linked by means of methods based on
artificial intelligence.
In a preferred embodiment, the improvement of the dynamic
model includes the identification of that input data which has
not yet been previously used by the dynamic model, and the
dynamic model can be expanded with the help of this input
data.
At the same time, the algorithm based on artificial
intelligence is used for the improvement of the dynamic model
for the identification and establishment of correlations not
yet considered in the dynamic model.
The dynamic model preferably includes one or more elements
from the group {characteristic, physical equation, neural
network, fuzzy logic, genetic algorithm}.
The dynamic model particularly includes at least one neural
network, which can be trained with historical operational data
from the system.
The modeling of technical components and facilities by means
of neural networks is a known and proven procedure. A
particular advantage to be seen therein is that an analytical
description of the components to be modeled heed not be known.
The structure of the neural network first initialized by means
of initial parameters ("initial weighting factors") and
determined in advance througn the training phase (which, for
example, includes a known backpropagation algorithm) is
designed with respect to its weighting factors such that a
good correlation with the actual component can be expected
after the conclusion of the training phase. In this manner one
obtains a model of the component without being required to
undertake a precise analytical analysis. In the training
phase, the neural network learns to respond to specific input
values with specific output values; together with their
corresponding output values, input values of this type are
often designated as a training set. In operation, the neural
network then interpolates for input values that are not
included in the training set, such that output values are also
calculated for input values of this type.
During operation of the technical facility, the problem often
appears that not all operational data that exerts an influence
on the behavior of the component(s) to be modeled (or the
entire technical facility as well) are known or ascertainable.
Furthermore, the use of at Least one algorithm based on
artificial intelligence makes it possible, by means of the
dynamic model, to incorporate into the calculations of the
status of the system of the technical facility those
parameters that do not act directly on this system of the
technical facility, for example as input and/or output signals
or media flows. For example, for a serially ordered chain of
systems, the modeling of a system located in the middle of
this chain is provided, which - alongside input signals where
appropriate acting directly upon this system - derive input
signals from the preceding system that are not measurable or
available in other ways.
At the same time, the methods of artificial intelligence
(which can be designed according to biological evolution, for
example, as genetic search algorithms based on a suitable
characteristic combination) a Lso them allow a calculation of
the status of a system of the technical facility if the input
parameters for the determination of the current status are
largely unknown or identifiable only with difficulty, for
example - as mentioned before - by means of a complex
measurement of the output values of the preceding system.
For example, statistical methods can also be used at the same
time in connection with the algorithm based on artificial
intelligence, whereby the most probable input and/or output
values for a system that are not otherwise accessible are used
.in a current operational situation in which the algorithm
based on artificial intelligence determines these input and/or
output values of the respective system which are required by
the dynamic model, for example, through an evolutionary search
strategy.
In this manner, a good correlation for the model of at least
one system can be expected with the actual behavior of this
system, because also included in the modeling of the system by
means of at least one algorithm based on artificial
intelligence is that operating data that would otherwise be
ignored and would lead to a more or less high level of
imprecision for the model and, for this reason especially, the
predictions created with it.
Input and/or output data for the system that determines its
operational status, but is not accessible, for example, by
means of measurement, is also included for this reason in
particular. Thus, the precision of the prediction is
increased.
A particularly preferred embodiment of the invention consists
of a number of analysis modules, which each include a dynamic
model of at least one system of the technical facility.
Furthermore, at least one additional algorithm based on
artificial intelligence is provided at the same time, by means
of which correlations at least between the input and/or output
data of a first of the analysis modules and the input and/or
output data of a second of the analysis modules is
identifiable.
This embodiment of the invention relates to the expansion of
the device according to the Invention to parallel monitoring
of interacting systems, whereby the interaction in the form of
a relationship between the respective input and/or output data
of the analysis modules is determined from additional
algorithms based on artificial intelligence and is established
as additional correlations (for example, in the form of an
equation, a neural network or a characteristic).
Thus develops a precise dynamic model of the interactive
systems comprising the dynamic model of the individual systems
as well as the additional correlations.
Thus, the current and/or future operational behavior of the
individual systems as well as the operational behavior of the
facility resulting from the interaction of the systems can be
described.
Advantageously identifiable at the same time by means of the
correlations is additional cutput data that characterizes the
current and/or future operational behavior of the technical
facility, whereby this additional output data includes cross-
system information.
Correlations between the said data indicate mutual
dependencies, by which the additional output data thus
extracted goes beyond the system limits of the individual
systems concerned in its informative value, and thus describes
the behavior of a larger unit of the technical facility
consisting of at least two systems.
Preferably, the operational and/or structural data of the
technical facility includes cne or more items of information
from the group {process data, operational messages, warning
messages, disruption messages, monitoring notifications,
comments, design of the technical facility, hierarchy of the
facility components}.
At the same time, the process data can be acquired online and
offline from a control system of the technical facility and/or
a subsystem associated with it, or also manually entered.
The operational messages particularly include sensor data and
information derived therefrom about the operational status of
the technical facility and its systems.
The structural data particularly includes information about
the design of the technical facility with regard to the
systems comprising the technical facility (facility
components, subsystems, system groups) as well as their
hierarchical interaction and prioritization.
At the same time, this data can include current and/or
historical data, which, for example, is recorded in a short-
or long-term archive or in an engineering system.
The operational and/or structural data is preferably supplied
by a process control system.
For operating and monitoring complex technical facilities, a
process control system in which the mentioned data is
available or accrues during operation and is stored is
typically used. In this embodiment, the data provision is
therefore of especially low complexity.
Furthermore, the invention leads to a method for monitoring a
technical facility comprising multiple systems, particularly a
power plant facility, including the following steps:
• Operational and/or structural data from the technical
facility is provided as input data to a dynamic model of at
least one system of the technical facility,
• the dynamic model of the system is improved during the
operation of the system by means of an algorithm based on
artificial intelligence, and
• output data that characterizes the current and/or future
operational behavior of the system is determined by means oi
the dynamic model.
The improvement of the dynamic model preferably includes the
identification of that input data that has not yet been
previously used by the dynamic model, and the dynamic model
can be expanded with the help of this input data.
In a further embodiment, a number of dynamic models are
provided that in each case describe at least one system of the
technical facility and at least one additional algorithm based
on artificial intelligence, by means of which correlations at
least between the input and/or output data of a first of the
dynamic models and the input and/or output data of a second of
the dynamic models are identifiable.
Advantageously identifiable by means of the correlations is
additional output data that characterizes the current and/or
future operational behavior of the technical facility, whereby
this additional output data includes cross-system information.
The explanations provided in connection with the device
according to the invention and its advantageous embodiments
are transferable to the method according to the invention and
are therefore not repeated here.
In summary, the invention can be incorporated into the
following environment:
Artificial intelligence fcr the diagnosis of systems of a
technical facility, for example a power plant facility, can be
used to predict foreseeable failures, whereby all data
available in the technical facility can be consulted.
At the same time, the main points of emphasis lie, for
example, in genetic algorithms and neural networks for
modeling and accomplishing the monitoring tasks, particularly
diagnosis tasks.
It is of particular interest to decidedly reduce the expense
of diagnosis applications in the technical facility, and
furthermore to make possible an improved optimization of the
controls.
An improvement is achieved of, on the one hand, the relevant
aggregate characteristics of systems of the technical
facility, for example, power and energy consumption, are
reduced with regard to legal regulations and resource
scarcity.
On the other hand, customer wishes for improved performance
and diagnosis facilities are fulfilled.
Both large systems and small systems can be integrated into
the diagnosis by means of genetic/evolutionary algorithms.
It is possible to facilitate conclusions about the status of
at least one system of the technical facility by associating
genetic (evolutionary) algorithms with Kohonen networks and/or
neural networks of any type.
The use of genetic algorithms therefore also makes it possible
to include in the determination of the status of a system of
the technical facility those parameters that do not directly
affect this component of the technical facility, for example
as input and/or output signals or media flows.
The methodologies of genetic algorithms (search algorithms)
therefore also then allow a calculation of the status of at
least one system or of the entire technical facility if the
input parameters for the determination of the current status
are largely unknown and/or not identifiable or identifiable
only with difficulty, for example, by means of an expensive
measurement.
Furthermore, the use of artificial intelligence for the
diagnosis makes it possible for deviations from calculated
current statuses to be reported to the operator of the
technical facility in the case of complex facility statuses.
A specific failure notice, for example about the narrowly
isolated failure location, can be initially dispensed with in
this case, because failures, for example of sensors, are
usually measured and reported in any event by an existing
control system.
What is instead important in connection with the invention is
the identification of creeping processes - that do not
necessarily cause immediate failure of a facility component -
such as contamination, loss of power through wear, ageing and
so forth, which are incorrectly perceived or incorrectly
interpreted by people because; of the "habituation effect".
In many cases, creeping changes of this type sometimes lead to
the failure of the technical facility. The changes are,
however, often not identified because an existing control
device, for example, attempts to counter this change.
Contamination on the blades of a fan is compensated for by re-
adjusting the fan blades, for example. Or the control device
compensates for decreased outputs in oil pumps or coolant
pumps through new reference value specifications; then the
temperature of a bearing, for example, becomes higher only
very slowly, because the control device can often delay the
moment of system failure in the case of an impending failure.
At the same time, however, ever more is demanded of the
controlled systems, and the wear increases. The user of the
technical facility notices nothing as a result, because,
according to the control device, the technical facility
continues to function even though one or more systems of the
technical facility get closer to their wear limit.
A risky operation exists in particular if a functioning system
is operated under increased stress; such stress can be
generated through a control device mentioned previously by
means of a reference value specification.
For example, a coolant loop is constructed for continuous
operation at 50% output. Permanent operation at 70-80% output
can then soon lead to serious damages. An impending failure of
a coolant pump stricken with a leak remains unnoticed,
however, because the control device increases the reference
value (i.e. the pressure) for the coolant pump more and more
to maintain the function of the coolant loop, which
accelerates the failure of the pump further. Not until the
failure actually occurs is the failure of the coolant pump
noticed as the source of the failure of the cooling system. A
device according to the invention as well as a method can
provide a remedy here.
Furthermore, genetic algorithms in conjunction with
intelligent, adaptive networks allow the identification of
risky operating modes of the technical facility, capacity
overload or incorrect capacity utilization of units and
systems, and so forth. This is advantageously reported to the
operator/user of the technical facility, for example in the
form of an operational diagram (i.e. a characteristic
diagram) , from which results the current operation as well as
a proposed improved operation.
The presentation of deviations can occur advantageously by
means of characteristic diagrams. In addition to the failure
prediction, an optimization of the operation of the operation
of the technical facility is also possible based on genetic
algorithms.
Furthermore, information can be extracted by means of genetic
algorithms for the management personnel of the technical
facility, which makes possibLe a conclusion about the overall
status of the facility and if appropriate about maintenance
procedures necessary within a time interval.
The use of artificial intelligence advantageously makes
possible an online calculation of system statuses, i.e. the
operator can be notified of a. "failure behavior" in his
facility and is then able to make anticipatory calculations
that make a new approach possible for him.
Example:
A device according to the invention, for example in the form
of a diagnostic system, reports "Failure in coal pulverizer XX
grinding rollers zone" to the operator; it is determined
through counterchecks that a maintenance procedure for the
coal pulverizer is necessary (for example, because this is
prescribed by the manufacturer in the related maintenance
manual) .
By means of anticipatory calculation, it can be determined by
means of the diagnostic system according to the invention what
would occur if the operator nevertheless leaves his technical
facility in operation without maintenance procedures and when
the actual appearance of an operational failure of the coal
pulverizer is to be expected.
With the combination of genetic algorithms and neural networks
as well as optional Kohonen networks, a multitude of
conclusions can be reached with respect to the current and/or
future status of the technical facility, in particular when a
maintenance procedure will oe necessary.
In the following two exemplary embodiments of the invention
are described in more detail.. They show:
FIG 1 a system hierarchy, as customarily occurs in technical
facilities,
FIG 2 a device according to the invention, and
FIG 3 a further embodiment c f a device according to the
invention with two analysis modules.
FIG 1 shows by way of example a hierarchical system design of
a technical facility 2.
The technical facility 2 is designed as a power plant facility
for the generation of electrical energy and includes two power
plant blocks 3.
Each power plant block 3 includes two turbines 5, for example
gas turbines. These turbines 5 in turn each contain a coolant
loop 9.
This coolant loop 9 includes a turbine blade 11 of the turbine
5.
Each of the mentioned elements should fall under the term
system in connection with the invention. A system can
therefore include a simple, isolated component such as, for
example a turbine blade, as well as a complex system, such as
the power plant block 3 or multiple power plant blocks 3.
FIG 2 shows a device 1 according to the invention with an
analysis module 13.
At the same time, operational data 17 and structural data 19
from the technical facility is conveyed to the analysis module
13 as input data.
The operational data 17 can for example involve online
measurement data which is recorded in the technical facility
in the system itself by means of sensors. At the same time, it
can also involve data derived from this measurement data,
which is produced in a computer system, for example.
Furthermore, the operationa] data 17 can also include offline
measurement data, which is stored in an archive or manually
entered, for example.
The structural data 19 describes the technical facility or the
system itself. In particular, it includes information about
the interconnection of subsystems that are included in the
system and their hierarchical arrangement.
A dynamic model 15 is proviced for modeling the system
behavior. This model 15 can include analytic equations, for
example, as well as methods of artificial intelligence such
as, for example, neural networks, fuzzy logic or genetic
algorithms. Furthermore, simple characteristics can in
particular be provided for the description of the system
behavior.
An algorithm 21 based on artificial intelligence is provided
for the improvement of the dynamic model 15 during the
operation of the system 15.
This algorithm 21 based on artificial intelligence can be
designed as a genetic algorithm, for example.
An important role for this algorithm 21 consists in effecting
dynamic adjustments in the model 15 in order to achieve an
improvement of this model 15 in the sense that an improved
model behavior and thus a better correlation with the behavior
of the actual system is achieved. For example, a modeling
error can be called upon for the evaluation of this
circumstance, for example, the difference between the actual
chronological behavior of the system and the modeled
chronological behavior of this system. An improvement of the
model 15 can then take place by means of the algorithm 21
based on artificial intelligence. At the same time, the
algorithm 21 based on artificial intelligence is particularly
used to identify parameters and data not yet considered during
modeling which are included in the operational data 10 and/or
the structural data 19 but have not yet been called upon for
modeling, and to establish further correlations, for example
equations or characteristics, including the mentioned
identified parameters and/or data, and to add them to the
dynamic model 15.
An algorithm 21 based on artificial intelligence designed as a
genetic algorithm optimizes correlations included in the
dynamic model 15 such as, for example, equations,
characteristics or network parameters of a neural network, in
that it combines and re-conbines evolutionary parameters and
data and, at the same time, discovers new correlations in
particular, which are not yet included in the dynamic model
15.
In this respect, the described modeling used in connection
with the invention and its improvement by means of the
algorithm 21 based on artificial intelligence goes beyond
known methods of, for exampLe, supervised learning and
classical modeling.
The analysis model 13 produces conclusions about the
operational behavior of the system as output data 23. At the
same time, for example, it can involve current or future
operational behavior of the system (creation of a prediction).
For example, operational data 17 is conveyed to the analysis
module 13, and it is assumed that this operational data will
persist over a particular future time period. The output data
23 then allows a conclusion, for example, as to whether and,
as the case may be, when a disruption of the system's
operation is to be expected. The more precisely the model 15
reflects the actual system behavior, the more precise is this
conclusion. A high level of precision for the model 15 is
provided by device 1 according to the invention, in particular
by means of the algorithm 21 based on artificial intelligence,
such that the predictions and diagnoses determined as output
data 23 by the analysis module 13 are very precise.
The output data 23 includes, in particular, qualified reports
with regard to failure identification (trend analysis, wear
and ageing), efficiency, process quality and expected future
behavior of the system and the technical facility.
In order to produce reports of this type, a set of rules can
be included in analysis module 13 in order to transform output
data generated from the model 15 into the mentioned reports.
At the same time, the set of rules can include rules for the
prediction of a short-tern observation period in particular as
well as rules for a long-term observation period.
At the same time, in addition to the output data from the
model 15, additional information can be conveyed to the set of
rules, for example reports and alarms related to the system or
the technical facility.
In the illustration for FIG 3, a device 1 according to the
invention includes two analysis modules 13a and 13b.
At the same time, operational data 17a and structural data 19a
from a coolant system 29 is conveyed to the analysis module
13a; the analysis module 13b receives operational data 17b and
structural data 19b from a generator 31 as input data.
In addition, environmental data 33 for the technical facility
is conveyed to both analysis modules 13a, 13b, for example the
ambient temperature, atmospheric humidity, atmospheric
pressure and so forth.
Each analysis module 13a, 13b detects output data 23a or 2 3b,
which characterizes the operational behavior of the respective
analyzed system 29 or 31.
Because the coolant system 29 and the generator 31 are to be
considered systems not procedurally isolated from one another,
it is to be anticipated that changing operational data 17a
from the coolant system 29 in particular influences the system
behavior of the generator 31, and thus the output data 23b
from the analysis module 13b. The same applies for changing
operational data 17b from the generator 31, from which it can
be expected that the operational behavior of the coolant
system 2 9 and thus the output data 23a from the analysis
module 13a changes as a result.
In order to detect and quantify correlations of this type, the
additional algorithm 25 based on artificial intelligence is
provided.
This can be designed, for example, as an additional genetic
algorithm that produces additional output data 27, which
includes cross-system information, and thus goes beyond the
characterization of the behavior of one of the systems, and in
particular contains information about the interaction of the
systems 29 and 31 and their mutual dependencies.
At the same time, the additional algorithm 25 based on
artificial intelligence is therefore responsible for
identifying and establishing higher-level cross-system
correlations. These correlations can include, for example,
equations, characteristics or neural networks, which are
produced and/or parameterized by the additional algorithm 25
based on artificial intelligence.
At the same time, the strategy for the identification and
establishment of cross-system correlations of this type can be
similar to the identification and establishment of additional
internal system correlations mentioned in connection with FIG
2 by means of the algorithms 21a, 21b based on artificial
intelligence.
By using a device according to the invention and a method
according to the invention, it should be possible, in
particular, to create conclusions about the system behavior,
in particular about that ir. the future, from a system's
existing operational and structural data without complex
diagnostic instruments.
A self-adaptive dynamic model of the system is provided for
this purpose, which is improved during operation by an
algorithm based on artificial intelligence.
The algorithm 21 based on artificial intelligence is used in
particular for searching for correlations in a technical
facility's operational and/or structural data that is
typically available and processed in a control system, for
example, and for integrating into the dynamic model the
correlations identified in doing so in order to improve the
said model incrementally.
It is therefore not necessary that an analytical model of the
system or of the technical .facility exists. Instead, the model
based on, for example, a very simple characteristic from a
characteristic array and/or on simple equations, is improved
incrementally by means of a correlation analysis of the
operational and structural data by means of the algorithm
based on artificial intelligence by establishing the
correlations determined in coing so, for example, in the form
of additional characteristics, equations, and so forth.
In contrast to conventional monitoring and diagnostic devices,
the present is preferably based on a data-based method,
whereby dependencies between parts of existing operational
data and/or between parts of structural data from a technical
facility are detected using methods of artificial intelligence
and established as quantified correlations, for example
equations and/or characteristics, such that a precise dynamic
model of at least one system of the technical facility is
produced.
Claims
1. Device (1) for monitoring a technical facility (2)'
comprising multiple systems, in particular a power plant
facility, characterized by
• at least one analysis module (13, 13a, 13b), which
includes a dynamic model (15) of at least one system (3,
5, 7, 9, 11) of the technical facility (2), whereby
operational (17, 17a, 17b) and/or structural data (19,
19a, 19b) from the technical facility (1) can be conveyed
to the analysis module (13, 13a, 13b) as input data, and
• at least one algorithm (21, 21a, 21b) based on artificial
intelligence included in the analysis module (13, 13a,
13b), by means of which the dynamic model (15) of the
system (3, 5/ 7, 9, 11) can be improved during the
operation of the system
whereby output data (23, 23a, 23b) is identifiable by means
of the analysis module (13, 13a, 13b) and characterizes the
current and/or future operational behavior of the system (3,
5, 7, 9, 11) .
2. Device (1) according to Claim 1, characterized in that the
improvement of the dynamic model (15) includes the
identification of that input data that has not yet been
previously used by the dynamic model (15), and in that the
dynamic model (15) can be oxpand€;d with the help of this
input data.
3. Device (1) according to Claim 1 or 2, in which the dynamic
model (15) includes one or more elements from the group
{characteristic, physical equation, neural network, fuzzy
logic, genetic algorithm}.
4. Device (1) according to one of the Claims 1 to 3, whereby
the dynamic model (15) includes at least one neural network,
which can be trained using historical operational data from
the system (3, 5, 7, 9, .1).
5. Device (1) according to one of the Claims 1 to 4,
characterized in that a number of analysis modules (13, 13a,
13b) are available, which include in each case a dynamic
model (15) of at least one system (3, 5, 7, 9, 11) of the
technical facility (2), and in that at least one additional
algorithm (25) based on artificial intelligence is provided,
by means of which correlc tions at least between the input
and/or output data of a first of the analysis modules (13,
13a, 13b) and the input and/or output data of a second of
the analysis modules (13, 13a, 13b) are identifiable.
6. Device (1) according to Claim 5, characterized in that
additional output data (27) is identifiable by means of the
correlations, said data characterizing the current and/or
future operational behavior of the technical facility (1) ,
whereby this additional cutput data (27) includes cross-
system information.
7. Device (1) according to one of the Claims 1 to 6, whereby
the operational data (17, 17a, 17b) and/or structural data
(19, 19a, 19b) of the technical facility (2) includes one or
more items of information from the group {process data,
operational messages, warning messages, disruption messages,
monitoring notifications, comments, design of the technical
facility, hierarchy of the facility components}.
8. Device (1) according to o.ie of the claims 1 to 7, whereby
the operational data (17, 17a, 17b) and/or structural data
(19, 19a, 19b) of the technical facility (2) includes
current and/or historical data from the technical facility
(2) .
9. Device (1) according to one of the Claims 1 to 8, whereby
the operational data (17,, 17a, 17b) and/or structural data
(19, 19a, 19b) from the technical facility (2) is provided
by a process control system of the technical facility (2).
10. Method for monitoring a technical facility (2) comprising
multiple systems, in particular a power plant facility,
characterized by the following steps:
• Operational data (17, 17a, 17b) and/or structural data
(19, 19a, 19b) from tie technical facility (2) is
conveyed to a dynamic model at least of one system (3, 5,
7, 9, 11) of the technical facility (2) as input data,
• the dynamic model (15) of the system (3, 5, 7, 9, 11) is
improved during the operation of the system (3, 5, 7, 9,
11) by means of an algorithm (21, 21a, 21b) based on
artificial intelligence, and
• by means of the dynamic model (15) , output data (27) is
identified which characterises the current and/or future
operational behavior of the system (3, 5, 7, 9, 11).
11. Method according to Claim 10, characterized in that the
improvement of the dynatmic model (15) includes the
identification of that input data which has not yet been
previously used by the dynamic model (15), and in that the
dynamic model (15) can be expanded with the help of this
input data.
12. Method according to one of the Claims 10 or 11,
characterized in that a number of dynamic models (15) are
provided, which in each case describe at least one system
(3, 5, 7, 9, 11) of the technical facility, and that at
least one additional algorithm (21, 21a, 21b) based on
artificial intelligence is, provided, by means of which
correlations at least between the input and/or output data
of a first of the dynamic models (15) and the input and/or
output data of a second of the dynamic models (15) are
identifiable.
3. Method according to Claim 12, characterized in that
additional output data is identifiable by means of the
correlations, said data characterizing the current and/or
future operational behavior of the technical facility (2),
whereby this additional output data includes cross-system
information.

The invention concerns a method and a corresponding device (1) for monitoring a technical installation (2). The
invention is characterized in that a dynamic model (15) of at least one system (3,5,7,9, 11) of the technical installation is enhanced
by means of an artificial intelligence based algorithm (21 21a, 21b) during the operation of said system (3,5,7, 9, 11).

Documents:

730-KOLNP-2005-CORRESPONDENCE.pdf

730-KOLNP-2005-FORM-27.pdf

730-kolnp-2005-granted-abstract.pdf

730-kolnp-2005-granted-claims.pdf

730-kolnp-2005-granted-correspondence.pdf

730-kolnp-2005-granted-description (complete).pdf

730-kolnp-2005-granted-drawings.pdf

730-kolnp-2005-granted-form 1.pdf

730-kolnp-2005-granted-form 2.pdf

730-kolnp-2005-granted-form 3.pdf

730-kolnp-2005-granted-specification.pdf

730-KOLNP-2005-PA.pdf

« Previous Patent

Next Patent »

Patent Number

229509

Indian Patent Application Number

730/KOLNP/2005

PG Journal Number

08/2009

Publication Date

20-Feb-2009

Grant Date

18-Feb-2009

Date of Filing

26-Apr-2005

Name of Patentee

SIEMENS AKTIENGESELLSCHAFT

Applicant Address

WITTELSBACHERPLATZ 2, 80333 MUNCHEN

Inventors:

#	Inventor's Name	Inventor's Address
1	FICK WOLFGANG	SCHÄFTLARNER STR. 130-20, 81371 MÜNCHEN
2	APPEL, MIRKO	PFALZER-WALD-STR. 47, 81539 MÜNCHEN
3	GERK UWE	ABTSDORF 64, 96158 FRENSDORF

PCT International Classification Number

G05B 23/02

PCT International Application Number

PCT/EP2003/007202

PCT International Filing date

2003-07-04

PCT Conventions:

#	PCT Application Number	Date of Convention	Priority Country
1	02021501.8	2002-09-26	EUROPEAN UNION