Cross working condition bearing fault diagnosis based on the combination of multimodal network and entropy conditional domain adversarial network

In the realm of intelligent fault diagnosis, fault diagnosis methods based on deep learning have been widely used and have achieved tremendous success. However, traditional single-modal fault diagnosis methods face challenges in terms of accuracy and reliability under conditions such as noise interference. To address this issue, this paper proposes a cross working condition rolling bearing fault diagnosis method based on the combination of multimodal network and entropy conditional domain adversarial network (ECDAN). Firstly, the time-domain signal is transformed into a time–frequency matrix through continuous wavelet transform (CWT), and then a deep feature extraction network is designed. This network integrates convolutional neural network (CNN) and 2D-ResNet18 to extract features from both time-domain signals and time–frequency matrices, and fuses these features. In order to enhance the transferability of learning features, the adversarial strategy of ECDAN is utilized to ensure alignment of bearing sample data between the source and target domains. Experimental validation on bearing dataset from the comprehensive fault simulation test platform for machinery demonstrates the effectiveness of the proposed method, indicating its capability to handle complex and variable working conditions as well as noise interference.