| Citation: | ZHANG Kangzhi. Fault diagnosis method of rolling bearing based on joint LLE and SSR[J]. Journal of Aerospace Power, 2024, 39（7）：20230263 doi: 10.13224/j.cnki.jasp.20230263
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A rolling bearing fault diagnosis method based on joint locally linear embedding and sparse self-representation (JLLESSR) and parameter-optimized support vector machine is proposed for rolling bearing vibration signals with strong nonlinearity and containing more redundant and irrelevant features, which leads to difficulties in extracting essential features and fault identification. The method constructs a unified feature extraction framework, relying on local linear embedding (LLE) to mine the local geometric structure of high dimensional data, and self-representation to mine the global geometric structure of high dimensional data in low dimensional space, to obtain the embedding features characterizing the operating state of rolling bearings. Then, the obtained features are fed into a cross-validation support vector machine (CV-SVM) for fault identification. Finally, the proposed method is tested on a rolling bearing fault data set, and the experimental results show that the proposed method can effectively identify different types of rolling bearing faults, and the fault diagnosis accuracy can reach 98.5%.
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