Abstract
Bearing fault diagnosis is of great significance for ensuring the safety of rotating electromechanical equipment. A deep learning network framework for diagnosing bearing faults under multiple load conditions is proposed to address the problems of extracting a single feature scale from bearing vibration timing signals, inability to simultaneously utilize spatial and bidirectional time features, and difficulty in obtaining sufficient training data under multiple working conditions. The first and second convolutional layers of a convolutional neural network (CNN) are used to simultaneously extract the spatio-temporal features from the bearing vibration signal and fuse them to obtain multi-scale spatiotemporal features. Based on this, BiLSTM is further applied to extract the bi-directional temporal correlation features of the input sequence. By introducing an attention mechanism (AM) to assign greater weights to critical spatio-temporal features, a new multi-scale deep learning network which integrates CNN, BiLSTM, and AM (MSCNN-BiLSTM-AM) network is proposed to obtain key bearing state features and accurate fault diagnose results. To further improve the adaptability of the network to different load conditions, the parameters of pretrained MSCNN-BiLSTM-AM network are applied to initialize the new task model parameters. After that, the new task diagnostic network is trained and validated under new load conditions by freezing the parameters of CNN, BiLSTM and AM layer, and fine-tuning the parameters of the fully connected layer and output layer. The experiments verify the excellent performance of the proposed method, while effectively solving the challenges of model training and fault diagnosis when there are insufficient training samples under multiple working conditions.