Research on rolling bearing fault diagnosis method based on improved multi-source fusion convolutional neural network

As sensor technology advances, the variety and number of sensors increase, leading to the capture of more signals. Existing multi-source fusion methods often face issues such as increased model complexity or the failure to fully utilize the potential correlations among multi-sensor data, thereby affecting the accuracy and reliability of fault diagnosis. To address this issue, this paper proposes a multi-source fusion convolutional neural network (MFCNN) that diagnoses bearing faults by integrating features from multi-source signals. Firstly, multiple convolution blocks with gradually increasing one-dimensional kernel sizes are utilized to extract features from the integrated multi-source data. This approach enhances feature extraction efficiency and simplifies the network architecture. Secondly, a feature fusion based on the convolutional block attention module attention mechanism is proposed, which refines feature representation through channel and spatial attention modules. This makes the model more focused on important information, thereby improving recognition accuracy. The diagnostic capabilities of the proposed MFCNN are evaluated utilizing two datasets.