Inferable Deep Distilled Attention Network for Diagnosing Multiple Motor Bearing Faults

Abstract

Bearing, as a vital component in electric powertrains, is increasingly used globally such as in electric vehicle (EV). Their damages and faults may bring huge cost loss to the industry and even threaten personal safety. This paper proposes an inferable deep distilled attention network (IDDAN) method which is a self-attention mechanism and transfer learning-based method to diagnose and classify multiple bearing faults in various motor drive systems efficiently and accurately. Compared with convolutional networks, the self-attention-based network can better extract the global feature information and easier to benefit from large amounts of pre-training data. Its significance is to accurately classify various faults of the target machine when the labeled data of the target machine is not enough to directly train the diagnosis model. Firstly, this paper attempt to apply the self-attention-based network to build an advanced fault diagnosis model. Secondly, this paper optimizes the structure of networks through knowledge distillation (KD) technique to require a lighter and fast model. Thirdly, this paper proposes a new data augmentation strategy for 1-D vibration signals to provide large-scale pre-training samples for IDDAN. Experiments show that the self-attention mechanism-based model is more likely to benefit from large-scale data. After testing, compared with many methods and other exist similar methods, the proposed method achieves higher classification accuracy and better performance.