Fault Diagnosis of Rotating Machinery Toward Unseen Working Condition: A Regularized Domain Adaptive Weight Optimization

Fault diagnosis under specific working conditions has achieved remarkable success. However, due to variations in working conditions, the assumption that training and test samples are independent and identically distributed is often violated, which makes the diagnostic model brittle under unseen working conditions. To this end, a generic generalization strategy, namely, regularized domain adaptive weight optimization strategy (RDAWOs), is devised for fault diagnosis of rotating machinery. We first design the architecture of a 1-D convolutional neural network. Then, the hyperparameter regularization term and an adaptive pooling layer are designed to control the complexity and improve the adaptability of the overparameterized deep model, respectively. Finally, domain adaptive weight optimization is established to identify the working condition abundant in spurious label-related information and to mine the robust fault knowledge under various working conditions. Obtained results indicate the strong generalization ability for out-of-distribution samples, as well as relatively high diagnostic accuracy of the RDAWOs-based deep model under unseen working conditions.