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| Bearing fault diagnosis based on shared neighbors weighted local linear embedding |
| LIU Qingqiang, SUN Yanru, LIU Yuanhong, WU Li |
| 1. School of Electrical and Information Engineering, Northeast Petroleum University, Daqing, Heilongjiang 163318, China; 2. Training Center of Natural Gas Branch, Daqing Oilfield Company Limited, Daqing, Heilongjiang 163457, China |
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Abstract To solve the problems that the neighbor distribution information of the samples was ignored for local linear embedding during mining the local manifold structure, and the default samples had the same importance in the dimensionality reduction process, leading to the inconspicuous feature extraction, the weighted local linear embedding based on shared neighbors (SN-WLLE) was proposed for bearing fault diagnosis. The cosine distance was adopted to divide the sample neighborhood in SN-WLLE. The sample neighborhood pair similarity was calculated by Jaccard coefficient to evaluate the sample shared neighbor information, and the local structure mining was modified by combining six neighbor distributions of the sample to improve the accuracy of the k-nearest neighbor reconstruction of multiple shared neighbors. The consistency of the sparse distribution between the sample and neighbors was evaluated to obtain the importance index of sample from the perspective of multi-manifolds, and the information was maintained in the low-dimensional space to extract accurate identification features. The complete bearing fault diagnosis model was constructed by combining KNN classifier. The bearing dataset of Case Western Reserve University and the bearing dataset of the laboratory test platform were used to analyze visual evaluation, quantitative clustering evaluation, fault identification accuracy evaluation and robustness evaluation. The results show that the F-value of SN-WLLE is maintained above 108, and the average fault identification accuracy can reach the minimum value of 0.973 4, which not only has good intra-class compactness and inter-class separability, but also has low sensitivity to the nearest neighbor parameter k.
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Received: 16 January 2022
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