Next Article in Journal
On the Complexity Analysis and Visualization of Musical Information
Previous Article in Journal
Flow Regime Recognition and Dynamic Characteristics Analysis of Air-Water Flow in Horizontal Channel under Nonlinear Oscillation Based on Multi-Scale Entropy
Previous Article in Special Issue
Gearbox Fault Diagnosis Based on Hierarchical Instantaneous Energy Density Dispersion Entropy and Dynamic Time Warping
Article Menu

Export Article

Open AccessArticle

A Fault Detection Method Based on CPSO-Improved KICA

Institute of Industrial Processes Intelligent Control, School of Automation, Wuhan University of Technology, Wuhan 430070, China
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;
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)
PDF [972 KB, uploaded 9 July 2019]
  |     |  


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

Figure 1

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).

Share & Cite This Article

MDPI and ACS Style

Liu, M.; Li, X.; Lou, C.; Jiang, J. A Fault Detection Method Based on CPSO-Improved KICA. Entropy 2019, 21, 668.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top