A fault detection optimization method based on chaos adaptive artificial fish swarm algorithm on distributed control system. (October 2018)

• Record Type: Journal Article
• Authors: Li, Teng-Hui; Xie, Shou-Sheng; Liu, Sheng-Ping; Xiao, Lei; Jia, Wei-Zhou; He, Da-Wei
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 232:Number 9(2018)
• Page Start: 1182
• Page End: 1193
• Publication Date: 2018-10
• Subjects: Fault detection optimization -- switching system -- artificial fish swarm algorithm -- chaos -- semi-physical platform; Mechanical engineering -- Periodicals; Automatic control -- Periodicals; Systems engineering -- Periodicals; 621.3
• DOI: 10.1177/0959651818777678
• Languages: English
• ISSNs: 0959-6518

Abstract

For the purpose of improving fault detection accuracy of aero-engine distributed control system, an optimal design method based on chaos adaptive artificial fish swarm algorithm for distributed control system fault detection observer was proposed. First, in order to better simulate the actual distributed control system, dual channel multiple packet transmission was converted to switching system and short time-varying delay was modeled as an uncertainty in system models. Then, a fault observer is designed to obtain the state-space model of error system and the chaos adaptive artificial fish swarm algorithm is introduced. Furthermore, the ratio value of the residual signal transfer function, respectively, to the unknown disturbance signal and fault signal, are regarded as the objective function, and the minimum of the objective function is optimized to get the optimal observer matrix by chaos adaptive artificial fish swarm algorithm with the constraint of error system stability. Finally, the structure and operating principle of aero-engine distributed control system semi-physical platform is introduced, and comparison is made between the simulation of fault detection optimization method and the tradition robust fault detection method on the platform. The simulation results verify that the proposed method can efficiently reduce fault positive ratio and fault negative ratio simultaneously, which can expand the selection scope of threshold value and improve accuracy of fault detection.