Signal-to-Signal Translation for Fault Diagnosis of Bearings and Gears With Few Fault Samples

Deep learning (DL)-based models can achieve satisfactory performance in bearing and gear fault diagnosis relying on sufficient fault vibration signals. However, it is difficult to acquire adequate machine fault data in industrial applications. Generally, data collected in the healthy condition are massive, but rare fault samples are available. To address the diagnostic problem with few fault data, this article proposes a method, namely, multilabel cycle translating adversarial network (MCTAN). The architecture can translate healthy vibration data into auxiliary fault data. Signal-to-signal translation enables auxiliary data to be acquired based on few fault data and adequate healthy data. Then, the auxiliary data are utilized to improve the performance of DL-based fault classification approaches. Experimental results and comparative analysis indicate the superiority and robustness of the proposed method for bearing and gear fault diagnosis on widely used XJTU-SY, Case Western Reserve University (CWRU), Jiangnan University (JNU), and Southeast University (SEU) datasets. Even if the ratio of healthy data to fault data ranges from 50:1 to 300:1, the proposed MCTAN succeeds in raising the diagnosis accuracy by up to 25.87% and outperforms other comparative methods.