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A new fault diagnosis and fault-tolerant control method for mechanical and aeronautical systems with neural estimators

Qi, H, Shi, Y, Tian, Y, Mayhew, C, Yu, DL, Gomm, JB and Zhang, Q (2019) A new fault diagnosis and fault-tolerant control method for mechanical and aeronautical systems with neural estimators. Advances in Mechanical Engineering, 11 (11). ISSN 1687-8132

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Abstract

A new method of fault detection and fault tolerant control is proposed in this paper for mechanical systems and aeronautical systems. The faults to be estimated and diagnosed are malfunctions occurred within the control loops of the systems, rather than some static faults, such as gearbox fault, component cracks, etc. In the proposed method two neural networks are used as on-line estimators, the fault will be accurately estimated when the estimators are adapted on-line with the post fault dynamic information. Furthermore, the estimated value of faults are used to compensate for the impact of the faults, so that the stability and performance of the system with the faults are maintained until the faulty components to be repaired. The sliding mode control is used to maintain system stability under the post fault dynamics. The control law and the neural network learning algorithms are derived using the Lyapunov method, so that the neural estimators are guaranteed to converge to the fault to be diagnosed, while the entire closed-loop system stability is guaranteed with all variables bounded. The main contribution of this paper to the knowledge in this field is that the proposed method cannot only diagnose and tolerant with constant fault, also diagnose and tolerant with the time-varying faults. This is very important because most faults occurred in industrial systems are time-varying in nature. A simulation example is used to demonstrate the design procedure and the effectiveness of the method. The simulation results are compared with two existing methods that can cope with constant faults only, and the superiority is demonstrated.

Item Type: Article
Uncontrolled Keywords: 0913 Mechanical Engineering
Subjects: T Technology > TK Electrical engineering. Electronics. Nuclear engineering
Divisions: Electronics & Electrical Engineering
Publisher: SAGE Publications
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Date Deposited: 06 Jan 2020 10:14
Last Modified: 06 Jan 2020 10:15
DOI or Identification number: 10.1177/1687814019891659
URI: http://researchonline.ljmu.ac.uk/id/eprint/11944

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