Fault Detection Based On Online Probability Density Function Estimation. Issue 6 (11th May 2016)

• Record Type: Journal Article
• Authors: Ghaniee Zarch, Majid; Alipouri, Yousef; Poshtan, Javad
• Is Part Of: Asian journal of control. Volume 18:Issue 6(2016)
• Page Start: 2193
• Page End: 2202
• Publication Date: 2016-05-11
• Subjects: Fault detection -- fuzzy logic -- kernel density estimation -- abrupt fault -- real‐time processing; Automatic control -- Periodicals; Control theory -- Periodicals; 629.805
• DOI: 10.1002/asjc.1314
• Languages: English
• ISSNs: 1561-8625

Abstract

Abstract: This study presents a new fault detection scheme based on the probability density function (PDF) of system output. Unlike the classical fault detection and diagnosis methods, in the proposed method, distribution of the system output is estimated online. To achieve this goal, an algorithm is introduced to estimate PDF online using fuzzy logic. Furthermore, convergence of this algorithm is investigated. Then, a residual is constructed that can show the existence of a fault in the system. The main advantages of the proposed method are robustness against measurement noise, even though it does not need the exact model and measured data of inputs and states. Simulation results show that this scheme can detect abrupt faults very well.