Fault diagnosis based on parameter estimation in closed‐loop systems. Issue 7 (1st April 2015)

• Record Type: Journal Article
• Authors: Zhai, Shouchao; Wang, Wei; Ye, Hao
• Is Part Of: IET control theory & applications. Volume 9:Issue 7(2015)
• Page Start: 1146
• Page End: 1153
• Publication Date: 2015-04-01
• Subjects: fault diagnosis -- fault tolerant control -- parameter estimation -- closed loop systems -- MIMO systems -- open loop systems -- matrix algebra -- reduced order systems; parameter fault diagnosis -- parameter estimation -- closed‐loop systems -- nonidentifiable closed‐loop multiple‐input multiple‐output systems -- open‐loop systems -- online identification -- parameter fault isolation -- parameter fault detection -- reduced‐order model -- orthogonal direction -- feature parameters -- physical fault detection -- physical fault isolation -- feature parameters -- projection direction -- influence matrix; Control theory -- Periodicals; Automatic control -- Periodicals; 629.8312
• DOI: 10.1049/iet-cta.2014.0717
• Languages: English
• ISSNs: 1751-8644

Abstract

Abstract : In this study, parameter faults in a class of non‐identifiable closed‐loop multiple‐input multiple‐output systems are considered. A new parameter estimation‐based fault diagnosis method is proposed. It is known that in open‐loop systems, the system parameters can be identified directly and the on‐line identification results can be used for fault detection and isolation. However, as the closed‐loop system is non‐identifiable because of the correlation introduced by the controller, unique optimal parameter estimation solution cannot be obtained. To address such an issue, a new method to detect and isolate parameter faults of closed‐loop systems without persistent excitation condition is proposed. A reduced‐order model is firstly constructed, which is the projection of the original model onto the orthogonal direction of the controller. By doing this, the aforementioned correlation can be successfully removed. The parameters of the newly constructed model, called as feature parameters, are then identified. The physical faults are finally detected and isolated based on the on‐line identification results of the feature parameters, the projection direction and the known influence matrix. Simulation results are given to show the effectiveness of the proposed method.