FDI algorithms of abrupt faults in controlled autoregressive processes. (English) Zbl 1145.93032
From the summary: The purpose of this paper is to present research in detecting and identifying abrupt faults in Controlled Auto-Regressive (CAR) processes.
Model-based approach is adopted in this paper. Two series of fault-tolerant iterative estimators are set up to estimate online the coefficients in a CAR process. Based on these fault-tolerant estimators, the detailed detecting and identifying algorithms are obtained for not only the pulse-type faults but also the step-type faults in CAR process.
This paper illustrates the useful information that can be obtained from residuals and that can be used to detect pulse-type faults as well as step-type faults. A fault-tolerant recursive estimator for the coefficients of the CAR process is put forward. Using a simple transformation from step- to pulse-type faults, all kinds of diagnosis methods to detect and identify step-type faults can be used.
Model-based approach is adopted in this paper. Two series of fault-tolerant iterative estimators are set up to estimate online the coefficients in a CAR process. Based on these fault-tolerant estimators, the detailed detecting and identifying algorithms are obtained for not only the pulse-type faults but also the step-type faults in CAR process.
This paper illustrates the useful information that can be obtained from residuals and that can be used to detect pulse-type faults as well as step-type faults. A fault-tolerant recursive estimator for the coefficients of the CAR process is put forward. Using a simple transformation from step- to pulse-type faults, all kinds of diagnosis methods to detect and identify step-type faults can be used.
Keywords:
safe design of controlled systems; detection and identifying abrupt faults; autoregressive processesReferences:
| [1] | DOI: 10.1016/0005-1098(88)90073-8 · Zbl 0653.93051 · doi:10.1016/0005-1098(88)90073-8 |
| [2] | Deng, Z. and Liu, Y. (2000), ”New algorithms of steady-state Kalman filter for stochastic control systems”, Acta Automatica Sinica, Vol. 26 No. 1, pp. 74-8. |
| [3] | Hilgert, N. and Vila, J. (1999), ”D-step ahead Kalman predictor for controlled autoregressive processes with random coefficients”, International Journal of Applied Mathematics and Computer Science, Vol. 9 No. 1, pp. 207-17. · Zbl 0923.93053 |
| [4] | Hu, S. and Karl, M. et al. (2006), ”Fault-tolerant fitting and online diagnosis of faults in SISO process”, Proceedings of 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes. |
| [5] | DOI: 10.1115/1.1589032 · doi:10.1115/1.1589032 |
| [6] | Yong Sheng, Z., Zhe Min, Y., Jie Yi, X. and Zhou, T-J. (2007), ”Multidimensional time series analysis based on support vector regression and controlled autoregressive and its application in ecology”, Acta Ecologica Sinica, Vol. 27 No. 6, pp. 2419-24. |
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.
