4.6 Article

An Improved Fault Diagnosis Approach Using LSSVM for Complex Industrial Systems

Journal

MACHINES
Volume 10, Issue 6, Pages -

Publisher

MDPI
DOI: 10.3390/machines10060443

Keywords

fault diagnosis; PSO; wavelet mutation; LSSVM

Funding

  1. National Natural Science Foundation of P.R. China [61663008, 62073051]
  2. Chongqing Technology Innovation, Application Special Key Project [cstc2019jscx-mbdxX0015]
  3. Reliable control and safety maintenance of dynamic system [JDDSTD2018001]

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This study proposes a novel approach for fault diagnosis in complex industrial systems by combining particle swarm optimization (PSO) with wavelet mutation and least square support vector machine (LSSVM). The method decomposes and reconstructs signals to extract fault features, and establishes a fault diagnosis model to improve classification accuracy. Experimental results show that the proposed approach outperforms traditional methods in terms of fault recognition efficiency.
Fault diagnosis is a challenging topic for complex industrial systems due to the varying environments such systems find themselves in. In order to improve the performance of fault diagnosis, this study designs a novel approach by using particle swarm optimization (PSO) with wavelet mutation and least square support (LSSVM). The implementation entails the following three steps. Firstly, the original signals are decomposed through an orthogonal wavelet packet decomposition algorithm. Secondly, the decomposed signals are reconstructed to obtain the fault features. Finally, the extracted features are used as the inputs of the fault diagnosis model established in this research to improve classification accuracy. This joint optimization method not only solves the problem of PSO falling easily into the local extremum, but also improves the classification performance of fault diagnosis effectively. Through experimental verification, the wavelet mutation particle swarm optimazation and least sqaure support vector machine ( WMPSO-LSSVM) fault diagnosis model has a maximum fault recognition efficiency that is 12% higher than LSSVM and 9% higher than extreme learning machine (ELM). The error of the corresponding regression model under the WMPSO-LSSVM algorithm is 0.365 less than that of the traditional linear regression model. Therefore, the proposed fault scheme can effectively identify faults that occur in complex industrial systems.

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