A domain adaptation model based on multiscale residual networks for aeroengine bearing cross-domain fault diagnosis

Abstract

As the core component of rotating machinery, the fault diagnosis of rolling bearing has important engineering practical significance. Most of the current intelligent fault diagnosis methods are based on the premise that the training data and test data have similar probability distributions. However, in practical scenarios, there will inevitably be discrepancies in the distribution of vibration signals due to internal and external factors such as changes in working conditions, which will significantly affect the diagnostic performance of the intelligent diagnostic model. Aiming at problems that the vibration signal characteristic distribution of rolling bearings is inconsistent under different working conditions and the labels of the samples to be diagnosed are difficult to obtain, a new domain-adaptive fault diagnosis method is proposed in this paper. Firstly, the multi-scale feature extraction module is used to extract the features of the input signals, and the residual network structure is used to avoid the degradation of the model performance. Then, the APReLU activation function is used to make the vibration signals perform different nonlinear transformations according to their own characteristics through adaptive learning. Finally, the Joint Maximum Mean Discrepancy (JMMD) is used to reduce the displacement of both conditional and edge distributions between different domains. Therefore, this method can extract domain-invariant feature information and align the source and target domains, which can be used for cross-domain intelligent fault diagnosis. Six transfer fault diagnosis tasks based on the rolling bearing experimental platform are designed to evaluate the performance and effectiveness of the proposed method. At the same time, four popular methods are selected for comprehensive analysis and comparison. The results show that the method has good robustness and superiority under various diagnostic tasks.