A rolling bearing fault classification method based on IGWO-SVM combined with center correction projection

LIU Yunhang1, SONG Yubo1, ZHU Dapeng2

Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (24) : 267-275.

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PDF(1571 KB)
Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (24) : 267-275.

A rolling bearing fault classification method based on IGWO-SVM combined with center correction projection

  • LIU Yunhang1, SONG Yubo1, ZHU Dapeng2
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Abstract

Aiming at the problems of global information loss and low mode recognition accuracy in rolling bearing fault classification, a rolling bearing fault classification method based on center modified projection (CMP) dimensionality reduction algorithm and support vector machine (SVM) optimized by improved Gray Wolf algorithm (IGWO) was proposed. Firstly, The dimension reduction algorithm of CMP was proposed by combining the global distribution information of the high-dimensional space and the local information of the sample. The dimension reduction of bearing signal feature matrix was realized by using the information retention ability of CMP. Secondly, the bell-shaped convergence curve of normal distribution and the forward search and bounding search modes were introduced to optimize the performance of the gray Wolf algorithm. The independent optimization of SVM parameters was realized by using the improved gray Wolf algorithm. Finally, the optimized SVM is used for bearing fault classification and recognition. This method fully combines the feature information retention ability of CMP with the small sample classification performance of SVM. The influence of multiple sets of comparative experiments show that this proposed method can effectively remove redundant components, better retain sample space distribution information, has a good classification performance.

Key words

Rolling bearing / Feature dimension reduction / Gray Wolf algorithm / Support vector machine / Fault classification

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LIU Yunhang1, SONG Yubo1, ZHU Dapeng2. A rolling bearing fault classification method based on IGWO-SVM combined with center correction projection[J]. Journal of Vibration and Shock, 2023, 42(24): 267-275

References

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