Yitong Xing

Given that the total labeled fault signals of rolling bearings are difficult to be collected, it is urgent to address the challenge of limited labeled vibration signals for intelligent fault diagnosis. In this case, the combination of artificial features and machine learning (ML) techniques is a potential way to handle this problem, because ML techniques have simple structures so that the large amounts of labeled signals are not demanded in the training process when compared to deep learning (DL)-based approaches. However, the artificial features usually have bias since they are based on experts’ subjective experience, which may leads to performance degradation of the ML-based approaches. To address this gap, a self-supervised framework of artificial feature bias rectification (SFAFBR) is proposed. Specifically, the considered artificial features of vibration signals are from wavelet packet (WP), which are combined with deep networks by serving as the guides to coax the deep networks learning. The learned features are closely relevant to the original artificial features but the bias is rectified due to the sparsely expression of deep model. Then, on the top of that, a ML-based classifier is constructed to perform the final diagnosis. The proposed framework achieved the state-of-the-art diagnosis accuracy of 97.79% and 94.84% on two bearing datasets with just 5 labeled samples. The result shows that using deep networks to assist in rectifying artificial feature bias is a promising way to address fault diagnosis problem of rolling bearings under limited labeled vibration signals.