Title :
Managing ignorance and uncertainty in system fault detection and identification
Author :
Vachtsevanos, G. ; Kang, H. ; Kim, I. ; Cheng, J.
Author_Institution :
Sch. of Electr. Eng., Georgia Inst. of Technol., Atlanta, GA, USA
Abstract :
A new approach to failure detection and identification (FDI) is proposed in order to address restructurable control systems. The methodology combines an analytic estimation method and an intelligent identification scheme in such a way that sensitivity to true failure modes is enhanced, while the possibility of false alarms is reduced. The authors employ a real-time recursive parameter estimation algorithm with covariance resetting, which triggers the FDI routine only when potential failure modes are anticipated. A possibilistic scheme based on fuzzy set theory is applied to the identification part of the FDI algorithm with computational efficiency. At the final stage of the algorithm, an index is computed-the degree of certainty-based on Dempster-Shafer theory, which measures the reliability of the decision. Simple simulation results demonstrate the effectiveness of the algorithm in managing uncertainty and ignorance
Keywords :
control system analysis computing; fuzzy set theory; parameter estimation; Dempster-Shafer theory; analytic estimation method; covariance resetting; failure detection and identification; fuzzy set theory; intelligent identification; real-time recursive parameter estimation algorithm; restructurable control systems; simulation results; system fault detection; system fault identification; uncertainty; Control systems; Electrical fault detection; Fault detection; Fault diagnosis; Filters; Fuzzy sets; Parameter estimation; Sequential analysis; System testing; Uncertainty;
Conference_Titel :
Intelligent Control, 1990. Proceedings., 5th IEEE International Symposium on
Conference_Location :
Philadelphia, PA
Print_ISBN :
0-8186-2108-7
DOI :
10.1109/ISIC.1990.128512