A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm
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Aiming at the problem that the most existing fault diagnosis methods could not effectively recognize the early faults in the rotating machinery, the empirical mode decomposition, fuzzy information entropy, improved particle swarm optimization algorithm and least squares support vector machines are introduced into the fault diagnosis to propose a novel intelligent diagnosis method, which is applied to diagnose the faults of the motor bearing in this paper. In the proposed method, the vibration signal is decomposed into a set of intrinsic mode functions (IMFs) by using empirical mode decomposition method. The fuzzy information entropy values of IMFs are calculated to reveal the intrinsic characteristics of the vibration signal and considered as feature vectors. Then the diversity mutation strategy, neighborhood mutation strategy, learning factor strategy and inertia weight strategy for basic particle swarm optimization (PSO) algorithm are used to propose an improved PSO algorithm. The improved PSO algorithm is used to optimize the parameters of least squares support vector machines (LS-SVM) in order to construct an optimal LS-SVM classifier, which is used to classify the fault. Finally, the proposed fault diagnosis method is fully evaluated by experiments and comparative studies for motor bearing. The experiment results indicate that the fuzzy information entropy can accurately and more completely extract the characteristics of the vibration signal. The improved PSO algorithm can effectively improve the classification accuracy of LS-SVM, and the proposed fault diagnosis method outperforms the other mentioned methods in this paper and published in the literature. It provides a new method for fault diagnosis of rotating machinery.
KeywordsIntelligent diagnosis Feature extraction Fuzzy information entropy Multi-strategy Particle swarm optimization Least squares support vector machines Combinatorial optimization
The authors would like to thank all the reviewers for their constructive comments. This research was supported by the National Natural Science Foundation of China (51475065, 51605068, 61771087, U1433124), Open Project Program of Sichuan Provincial Key Lab of Process Equipment and Control (GK201613), Open Project Program of the Traction Power State Key Laboratory of Southwest Jiaotong University (TPL1705), Natural Science Foundation of Liaoning Province (2015020013, 20170540126, 20170540125), and Science and Technology Project of Liaoning Provincial Department of Education (JDL2016030). The program for the initialization, study, training, and simulation of the proposed algorithm in this article was written with the toolbox of MATLAB 2010b produced by the MathWorks, Inc.
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Conflict of interest
The authors declare that they have no conflicts of interest.
This article does not contain any studies with human participants performed by any of the authors.
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