A novel bearing fault diagnosis method for compound defects via zero-shot learning

In recent years, deep learning-based bearing fault diagnosis methods have made significant achievements. However, these methods only work with single faults and cannot diagnose compound faults because compound fault data is often unavailable in practice. To address this problem, this paper proposes a zero-shot learning-based bearing fault diagnosis method for compound defects. The proposed method utilizes an autoencoder network to observe the attributes of single faults and then estimates the attributes of compound faults. Afterward, a mapping from the data space to the attribute space is established to predict the attribute output of the data. The attribute output is then compared with prior attributes to determine the type of bearing fault. Verification experiments were conducted on HUST bearing dataset. The experimental results showed that the proposed method achieved a high accuracy of 75.64 % in diagnosing compound bearing faults.