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Process fault detection based on modeling and estimation methods - a survey. (English) Zbl 0539.90037
Summary: The supervision of technical processes is the subject of increased development because of the increasing demands on reliability and safety. The use of process computers and microcomputers permits the application of methods which result in an earlier detection of process faults than is possible by conventional limit and trend checks. With the aid of process models, estimation and decision methods it is possible to also monitor nonmeasurable variables like process states, process parameters and characteristic quantities. This contribution presents a brief summary of some basic fault detection methods. This is followed by a description of suitable parameter estimation methods for continuous-time models. Then two examples are considered, the fault detection of an electrical driven centrifugal pump by parameter monitoring and the leak detection for pipelines by a special correlation method.

MSC:
90B25 Reliability, availability, maintenance, inspection in operations research
62N05 Reliability and life testing
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[1] Baskiotis, C.; Raymond, J.; Rault, A., Parameter identification and discriminant analysis for jet engine mechanical state diagnosis, ()
[2] Baskiotis, C.; Brault, J.P.; Derekx, Y.; Rault, A.; Videau, J.A., Sûreté de fonctionnement des systèmes physiques, Journées SURF, 196, (1981)
[3] Beard, R.V., Failure accommodation in linear systems through self-reorganization, ()
[4] Billmann, L., A method for leak detection and localization in gas pipelines, (1983), Applied Control and Identification, ACI Copenhagen
[5] Billmann, L.; Isermann, R., Leak detection methods for pipelines, () · Zbl 0616.93067
[6] Candy, J.V.; Rozsa, R.B., Safeguards design for a plutonium concentrator—an applied estimation approach, Automatica, 16, 615, (1980)
[7] De Keyser, R.M.C.; van Cauwenberghe, A.R., A self-tuning multistep predictor application, Automatica, 17, 167, (1981)
[8] Digernes, T., Real-time failure detection and identification applied to supervision of oil transport in pipelines, (), 39-49
[9] Filbert, D.; Metzger, K., Quality test of systems by parameter estimation, ()
[10] Geiger, G., Monitoring of an electrical driven pump using continuous-time parameter estimation methods, ()
[11] Geiger, G., Fault identification of a motor-pump system using parameter estimation and pattern classification, ()
[12] Himmelblau, D.M., ()
[13] Hohmann, H., Automatic monitoring and failure diagnosis for machine tools, (), (in German)
[14] Hung, J.C.; Liu, C.C.; Chou, P.Y., ()
[15] Isermann, R., Fault detection methods for the supervision of technical processes, (), 36-44
[16] Isermann, R., ()
[17] Isermann, R.; Siebert, H., Verfahren zur leckerkennung und leckortung bei rohrleitungen, (1976), Patent P 2603 715.0
[18] Isermann, R.; Siebert, H., A method for the detection and localisation of small leaks in gas pipelines, ()
[19] Jones, H.L., Failure detection in linear systems, ()
[20] Mehra, R.K.; Peschon, J., An innovations approach to fault detection and diagnosis in dynamicsystems, Automatica, 7, 637, (1971)
[21] Montgomery, R.C.; Caglayan, A.K., A selfreorganizing digital flight control system for aircraft, ()
[22] Montgomery, R.C.; Price, D.B., Management of analytical redundancy in digital flight control systems for aircraft, ()
[23] Pau, L.F., ()
[24] Pfleiderer, C., ()
[25] Saedtler, E., Hypothesis testing and system identification methods for on-line vibration monitoring of nuclear power reactors, ()
[26] Siebert, H., Evaluation of different methods for pipeline leakage monitoring, Kernforschungszentrum karlsruhe, PDV-report, kfk-PDV, 206, (1981), (in German)
[27] Siebert, H.; Isermann, R., Leckerkennung und - lokalisierung bei pipelines durch on-line-korrelation mit einem prozeßrechner, Regelungstechnik, 25, 69, (1977)
[28] Siebert, H.; Isermann, R., A method for the detection and localization of small leaks in gas pipelines, ()
[29] Siebert, H.; Klaiber, Th., Testing a method for leakage monitoring of a gasoline pipeline, (), 232-237, (1980), In German
[30] Sinha, N.K.; Lastman, G.J., Identification of continuous time multivariable systems from sampled data, Int. J. control, 35, 117, (1982) · Zbl 0473.93071
[31] Strmčnik, S.; Bremsak, F., Some new transformation algorithms in the identification of continuous-time multivariable systems using discrete identification methods, () · Zbl 0564.93019
[32] ()
[33] Willsky, A.S., A survey of design methods for failure detection systems, Automatica, 12, 601, (1976) · Zbl 0345.93067
[34] Willsky, A.S., Failure detection in dynamic systems, Agard no. 109, (1980)
[35] Willsky, A.S.; Jones, H.L., A generalized likelihood ratio approach to state estimation in linear systems subject to abrupt changes, ()
[36] Young, P.C., An instrumental variable method for real-time identification of a noisy process, Automatica, 6, 271, (1970)
[37] Young, P.C., Parameter estimation for continuous-time models—a survey, Automatica, 17, 23, (1981) · Zbl 0451.93052
[38] Young, P.C.; Jakeman, A., Refined instrumental variable methods of recursive time-series analysis. part III. extensions, Int. J. control, 31, 741, (1980) · Zbl 0468.93089
[39] Zwingelstein, G.C.; Upadhyaya, B.R., Identification of multivariate models for noise analysis of nuclear plant, ()
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