| Citation: | KANG Yuxiang, CHEN Guo, WANG Hao, et al. Early fault detection of rolling bearings based on self-supervised deep one classification[J]. Journal of Aerospace Power, 2025, 40（3）：20220652 doi: 10.13224/j.cnki.jasp.20220652
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At present the intelligent fault diagnosis model is difficult to train due to the difficulty in obtaining the fault data of rolling bearings. A self-supervised deep one-class classification method was proposed for early fault warning of rolling bearings based on the training of normal class samples. Based on the deep one-class classification model, multi-task and self-supervision mechanisms were introduced. Input signal was extracted with the use of the depth of the deep residual network characteristics, and the proposed features were taken respectively as input of more child tasks, including support vector description (SVDD) as the output results of the classification subtasks supervision and label as the rest of the subtasks; through the joint loss function, only relying on normal class samples can complete the model of supervised learning. When the proposed method was applied to the fault warning of rolling bearings, the vibration acceleration signals of the whole life cycle were decomposed by frequency band, and the signals in different frequency bands were encoded as the input of the network. The proposed method was validated on two actual rolling bearing fault data sets. The verification results showed that the accuracy of the proposed depth classification method reached more than 99% in fault warning, which fully indicated that the proposed method has a high ability of fault warning and anomaly detection.
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