Cross-Sensor Correlative Feature Learning and Fusion for Intelligent Fault Diagnosis

With the maturity of big data and computing power, deep learning has provided an end-to-end efficient solution for fault diagnosis of rotating machinery. However, the diagnosis performance is commonly affected by complex working environment and limited labeled samples. While considering these undesirable effects and borrowing from multisource fusion techniques, we propose a novel fault diagnosis method based on cross-sensor correlative feature learning and fusion. First, global-local temporal encoder is utilized to learn the time-domain features of multiple sensor data. Meanwhile, time-frequency encoder is performed to obtain the corresponding time-frequency domain features. Then, features of the two modes are fused to get the initial results. Finally, they are put through cross-sensor correlative channel-aware fusion to achieve a final result. Furthermore, two datasets are selected to verify the effectiveness of the proposed method. The results demonstrate that our method is effective, robust, and suitable for diagnosis under limited data and complex conditions.