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A Fault Detection Method Based on CPSO-Improved KICA

1
Institute of Industrial Processes Intelligent Control, School of Automation, Wuhan University of Technology, Wuhan 430070, China
2
Department of Electrical and Computer Engineering, Western University, London, ON N6A 5B9, Canada
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(7), 668; https://doi.org/10.3390/e21070668
Received: 29 May 2019 / Revised: 26 June 2019 / Accepted: 5 July 2019 / Published: 9 July 2019
(This article belongs to the Special Issue Entropy-Based Fault Diagnosis)
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Abstract

In view of the randomness in the selection of kernel parameters in the traditional kernel independent component analysis (KICA) algorithm, this paper proposes a CPSO-KICA algorithm based on Chaotic Particle Swarm Optimization (CPSO) and KICA. In CPSO-KICA, the maximum entropy of the extracted independent component is first adopted as the fitness function of the PSO algorithm to determine the optimal kernel parameters, then the chaotic algorithm (CO) is used to avoid the local optimum existing in the traditional PSO algorithm. Finally, this proposed algorithm is compared with Weighted KICA (WKICA) and PSO-KICA with Tennessee Eastman Process (TEP) as the benchmark. Simulation results show that the proposed algorithm can determine the optimal kernel parameters and perform better in terms of false alarm rates (FAR), detection latency (DL) and fault detection rates (FDR). View Full-Text
Keywords: fault detection; KICA; the maximum entropy; CPSO fault detection; KICA; the maximum entropy; CPSO
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Liu, M.; Li, X.; Lou, C.; Jiang, J. A Fault Detection Method Based on CPSO-Improved KICA. Entropy 2019, 21, 668.

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