Rotating machinery fault diagnosis based on transfer learning and an improved convolutional neural network
Li Jiang, Chunpu Zheng, Yibing Li
Abstract
Abstract Deep-learning-based methods have been widely used for rotating machinery fault diagnosis. However, they exhibit poor performance due to the severe data distribution difference under variable working conditions. Therefore, we first develop an improved convolutional neural network consisting of a multi-scale convolutional layer (MSC), a channel attention layer (CA), and an inception network structure (INS). Compared with other models, our model has strong feature extraction ability, fewer parameters and less training costs. Subsequently, based on transfer learning (TL), we propose the MSC-CA-INS-TL method. To improve the model’s generalization ability, we propose an appropriate fine-tuning strategy to coordinate with the model and pay attention to the accuracy of both source and target domains during migration. Bearing datasets and gear experimental platforms are used to verify the proposed method, and high fault diagnosis accuracy and stability are achieved under variable working conditions and small samples.