Fault diagnosis of rolling bearings based on ISSA-SVM

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Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (6) : 106-114.

LI Xinran，JIN Wuyin

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Abstract

Aiming at the problems that swarm intelligence algorithm optimization Support Vector Machine model (SVM)is easy to fall into local optimum and low accuracy in the field of rolling bearing fault diagnosis, a method of optimizing Support Vector Machine (SVM) based on improved Sparrow Search Algorithm (SSA) is proposed for fault diagnosis of rolling bearings. First, the evenly distributed Chebyshev chaotic map is introduced to initialize the sparrow population in order to improve the spatial distribution uniformity of the population. Then, the adaptive inertia weights are integrated into the location update of the discoverer of the sparrow algorithm. Finally, the optimal sparrow after the updated position is randomly disturbed to improve the global and local search ability of the algorithm and avoid falling into the local optimization. The algorithm is applied to the parameter optimization of support vector machine, and the ISSA-SVM fault diagnosis model is constructed to realize the classification and diagnosis of bearing fault signals. The analysis results of the rolling bearing fault diagnosis test show that the fault classification effect of the ISSA-SVM model is obviously better than that of the PSO-SVM, GA-SVM and SSA-SVM models, which can effectively identify the types of faults of rolling bearings.

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fault diagnosis / rolling bearing / random walk / Sparrow search algorithm(SSA) / support vector machine(SVM)

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LI Xinran，JIN Wuyin. Fault diagnosis of rolling bearings based on ISSA-SVM[J]. Journal of Vibration and Shock, 2023, 42(6): 106-114

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