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IZOA-SVM algorithm with parameter optimization for mechanical equipment fault diagnosis

Published:2024-10-30 author:ZHAO Yuejing, XING Tianxiang, QIN Zhiying. Browse: 185 Check PDF documents

IZOA-SVM algorithm with parameter optimization for mechanical

equipment fault diagnosis

ZHAO Yuejing, XING Tianxiang, QIN Zhiying

(School of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China)

Abstract: In complex working environment, the complexity of vibration signals of mechanical equipment often leads to low accuracy of fault diagnosis. In order to solve the problem of fault diagnosis caused by signal complexity in equipment operation, a fault diagnosis method of support vector machine optimized by zebra optimization algorithm (IZOA-SVM) with parameter optimization was proposed. Firstly, the improved strategies of Cauchy variation and reverse learning were introduced into the zebra optimization algorithm (ZOA), and the improved zebra optimization algorithm (IZOA) was proposed, it aimed to improve the local extreme value problem of the original zebra optimization algorithm in the late iteration, so as to effectively enhance its global search capability. Secondly, the kernel parameter g and penalty parameter c of support vector machine (SVM) were optimized by IZOA to find the optimal combination of SVM parameters ［c, g］, and the IZOA-SVM model was constructed. Then, 13-time domain features of the sample were calculated to form the feature vector. The eigenvectors were input to IZOA-SVM model, support vector machine optimized by zebra optimization algorithm (ZOA-SVM) model, particle swarm optimization support vector machine (PSO-SVM) model,
support vector machine optimized by genetic algorithm (GA-SVM) model and support vector machine model (SVM)for fault classification. Finally, the effectiveness of the method was verified by the vibration and fault simulation tests of rotating machinery. The results show that the fault accuracy of IZOA-SVM model is 98.33%, which is improved in different degrees comparing with other models. Therefore, the method has achieved obvious improvement in the accuracy of global search and fault classification, and provides a reference solution for fault diagnosis in complex working environments.

Key words: mechanical equipment; rotary machine; fault diagnosis; improved zebra optimization algorithm (IZOA); Cauchy variation; reverse learning; support vector machine (SVM)

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