Parameter adaptive analysis of rolling bearing fault based on QGA optimization

Yang Zhang; Xizhong Shen

doi:10.1117/12.2691194

1 August 2023 Parameter adaptive analysis of rolling bearing fault based on QGA optimization

Yang Zhang, Xizhong Shen

Proceedings Volume 12752, Second International Conference on Optoelectronic Information and Computer Engineering (OICE 2023); 1275204 (2023) https://doi.org/10.1117/12.2691194
Event: Second International Conference on Optoelectronic Information and Computer Engineering (OICE 2023), 2023, Hangzhou, China

Abstract

In response to the influence of parameter setting on the extraction of fault feature frequency of mechanical trouble signal by variational mode decomposition (VMD), a study on trouble signal feature extraction based on Quantum Genetic Algorithm (QGA) optimized variational mode decomposition was constructed. First, take the envelope entropy as a fitness feature, and use the quantum genetic algorithm to calculate the two parameters [K, α] of VMD are selected adaptively. Secondly, the VMD with the optimized parameter [K, α] is used to decompose the fault vibration signal, and multiple intrinsic mode components (IMF) are obtained. Finally, Hilbert envelope spectrum analysis was carried out for the modal component with the lowest envelope entropy, and can effectively determine the fault type through the fault characteristic frequency. It is proved that this method is useful and can solve the effect of parameter setting on VMD.

Citation Download Citation

Yang Zhang and Xizhong Shen "Parameter adaptive analysis of rolling bearing fault based on QGA optimization", Proc. SPIE 12752, Second International Conference on Optoelectronic Information and Computer Engineering (OICE 2023), 1275204 (1 August 2023); https://doi.org/10.1117/12.2691194

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