Generalization of parity space to fault detection based on Takagi‐Sugeno fuzzy models for non‐linear dynamic systems. Issue 1 (31st July 2017)

• Record Type: Journal Article
• Authors: Ghaniee Zarch, Majid; Aliyari Shoorehdeli, Mahdi
• Other Names: Rocha Álvaro guestEditor.; Lima Stanley guestEditor.
• Is Part Of: Expert systems. Volume 35:Issue 1(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-07-31
• Subjects: fault detection -- non‐linear systems -- parity space -- Takagi‐Sugeno fuzzy model; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12228
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: Fault detection in non‐linear system has drawn a lot of attention recently. A typical solution is the generalization of linear methods to include non‐linear dynamics. This study addresses fault detection in non‐linear systems by extending parity relations using Takagi‐Sugeno (T.S) fuzzy models. Parity equations for linear systems are a residual generation method that has appealing capabilities in fault detection. T.S fuzzy systems are also extensively used in modelling of non‐linear systems. In this paper, parity equations are rewritten in the form of non‐linear systems that can be modelled by T.S fuzzy system. An advantage of this approach is that parity vector can be derived from relations explicitly. An algorithm is proposed to show how a residual can be generated in this manner. Simulation results on the fault detection of a mass‐spring‐damper system show the effectiveness of the proposed method.