- Email: [email protected]
or Model Based Diagnosis
Copyright © IFAC Fault Detection, Supervision and Safety for Technical Processes, Baden-Baden, Germany, 1991
SIGNAL AND/OR MODEL BASED DIAGNOSIS
Chairman :
Prof. D. Barschdorff, Paderborn, Germany
Panelists:
Prof. P.M. Frank, Duisburg, Germany Prof. R.Isermann, Darmstadt, Germany Dr. R. 1. Patton, York, U.K. Dr.-Ing. D. Wach, Garching, Germany
After a brief introductory statement of Professor Barschdorff outlining the basic principles of signal based and model based fault diagnosis, the panel members gave short introductions from their point of view. Professor Isermann summarized recent results of research concerning model based diagnosis. "What are faults, where are they coming from? Do we have models for fault description'!" were his questions. Comparing signal models, parametric and non parametric ARMA type models, mathematical modelling utilizing parameter estimation and process state estimation, he outlined the advantages of model based diagnosis, especially parameter estimation. While "simple" signal based diagnosis might only be useful for detection of vibrations and related faults, Mr. Isermann stated, model based methods should allow a greater depth of diagnosis.
Dr. Patton mentioned, that parameter based methods need more information about the process than state estimation methods, but both methods are of comparable efficiency in fault diagnosis. He stated, that the model based diagnosis could be regarded as a special case of the signal based diagnosis. Also, it might be dangerous to use the FIT without information of the model. Sometimes, though faults are not yet visible, deviations of state behaviour are developping. For signal based diagnosis, signal and sensor redundancy and for the model based diagnosis signal as well as analytical redundancy are required. In the following discussion members from the audience joined in with statements and remarks. One participant did not agree with the distinction between signal based and model based diagnosis. Instead, he suggested to distinguish between model free and model based diagnosis. In the industry mostly the terms "transfer functions" are used, they are well established in the car industry. It was also stated, that parameter based methods are easier to understand than state estimation methods from people in industry. Often the question arises how to derive an adequate process model and usually much time is required for the model development, which is not available in industrial daily routine. Test cycles of 5 to 10 seconds are necessary in quality control application. It was also mentioned, that model based methods utilize signals, while signal based methods use models to interprete the signal structure. Finally, the observer method as general input/output description of a system was brought to attention. However, in diagnosis the real physical properties of a system have to be identified in order to identify the sources of faults within a technical system. Professor Patton finally suggested a benchmark test with real process data in order to test and verify the different approaches.
Professor Frank also emphasized the advantages of model based diagnosis. However, he outlined a major difference in the state estimation and the parameter estimation approach . Under system redundancy requirements he favoured the state estimation method. "How can parameters be identified, when no changes occur in system output'! Do faults change the system state? Are observers for static states useful?" Input signals of the process are needed for stimulation. Both methods, parameter as well as state estimation can be regarded as being complementary. Dr. Wach acknowledged the benefits of the model based approach, if mathematical models of the process are available. Also, for an early failure detection the model based methods may be advantageous. Very often, however, no modelling of a process is possible and yet a diagnosis has to be performed. Signal oriented fault diagnosis also uses models, to interpret the signal structure and the information content. Deviations and changes of the computed features are then analysed and interpreted as failure states. Compared with the signal based approach, mathematical models are often too complex to be used in on-line fault diagnosis. During former times of "analog techniques", information processing was reduced to monitoring of RMS signals only, therefore often only one signal was to be monitored for the purpose of failure detection. Nowadays the possibility exists to use advanced computer equipment and a much more detailed signal analysis is possible. In the case of a low signal to noise ratio. sensor signals have to be measured against high background noise. Also. the sensor response time plays a major role. It should be realised, that signal based failure diagnosis methods are in manifold practical use and the operators do understand the method. However, false alarms are the death of any fault diagnosis system.
Professor Barschdorff thanked all participants of the roundtable discussion and briefly summarized the results of the contributions. It was demonstrated clearly, that the signal based as well as the model based approach combined with pattern recognition methods have demonstrated to obtain a great diagnosis depth in various technical applications.
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SIGNAL AND/OR MODEL BASED DIAGNOSIS
Chairman :
Prof. D. Barschdorff, Paderborn, Germany
Panelists:
Prof. P.M. Frank, Duisburg, Germany Prof. R.Isermann, Darmstadt, Germany Dr. R. 1. Patton, York, U.K. Dr.-Ing. D. Wach, Garching, Germany
After a brief introductory statement of Professor Barschdorff outlining the basic principles of signal based and model based fault diagnosis, the panel members gave short introductions from their point of view. Professor Isermann summarized recent results of research concerning model based diagnosis. "What are faults, where are they coming from? Do we have models for fault description'!" were his questions. Comparing signal models, parametric and non parametric ARMA type models, mathematical modelling utilizing parameter estimation and process state estimation, he outlined the advantages of model based diagnosis, especially parameter estimation. While "simple" signal based diagnosis might only be useful for detection of vibrations and related faults, Mr. Isermann stated, model based methods should allow a greater depth of diagnosis.
Dr. Patton mentioned, that parameter based methods need more information about the process than state estimation methods, but both methods are of comparable efficiency in fault diagnosis. He stated, that the model based diagnosis could be regarded as a special case of the signal based diagnosis. Also, it might be dangerous to use the FIT without information of the model. Sometimes, though faults are not yet visible, deviations of state behaviour are developping. For signal based diagnosis, signal and sensor redundancy and for the model based diagnosis signal as well as analytical redundancy are required. In the following discussion members from the audience joined in with statements and remarks. One participant did not agree with the distinction between signal based and model based diagnosis. Instead, he suggested to distinguish between model free and model based diagnosis. In the industry mostly the terms "transfer functions" are used, they are well established in the car industry. It was also stated, that parameter based methods are easier to understand than state estimation methods from people in industry. Often the question arises how to derive an adequate process model and usually much time is required for the model development, which is not available in industrial daily routine. Test cycles of 5 to 10 seconds are necessary in quality control application. It was also mentioned, that model based methods utilize signals, while signal based methods use models to interprete the signal structure. Finally, the observer method as general input/output description of a system was brought to attention. However, in diagnosis the real physical properties of a system have to be identified in order to identify the sources of faults within a technical system. Professor Patton finally suggested a benchmark test with real process data in order to test and verify the different approaches.
Professor Frank also emphasized the advantages of model based diagnosis. However, he outlined a major difference in the state estimation and the parameter estimation approach . Under system redundancy requirements he favoured the state estimation method. "How can parameters be identified, when no changes occur in system output'! Do faults change the system state? Are observers for static states useful?" Input signals of the process are needed for stimulation. Both methods, parameter as well as state estimation can be regarded as being complementary. Dr. Wach acknowledged the benefits of the model based approach, if mathematical models of the process are available. Also, for an early failure detection the model based methods may be advantageous. Very often, however, no modelling of a process is possible and yet a diagnosis has to be performed. Signal oriented fault diagnosis also uses models, to interpret the signal structure and the information content. Deviations and changes of the computed features are then analysed and interpreted as failure states. Compared with the signal based approach, mathematical models are often too complex to be used in on-line fault diagnosis. During former times of "analog techniques", information processing was reduced to monitoring of RMS signals only, therefore often only one signal was to be monitored for the purpose of failure detection. Nowadays the possibility exists to use advanced computer equipment and a much more detailed signal analysis is possible. In the case of a low signal to noise ratio. sensor signals have to be measured against high background noise. Also. the sensor response time plays a major role. It should be realised, that signal based failure diagnosis methods are in manifold practical use and the operators do understand the method. However, false alarms are the death of any fault diagnosis system.
Professor Barschdorff thanked all participants of the roundtable discussion and briefly summarized the results of the contributions. It was demonstrated clearly, that the signal based as well as the model based approach combined with pattern recognition methods have demonstrated to obtain a great diagnosis depth in various technical applications.
621