Exploring Informative and Highly-Transferable Features for Cross-Machine Fault Diagnosis by ConvFormer-Based Biconditional Domain Adaptation Method

Abstract

Domain adaptation-based methods have been proved success for cross-machine fault diagnosis. However, such methods suffer from limited diagnosis performance because the utilized networks typically rely on convolution layers with local receptive fields, failing to extract informative fault features, and the information on machine domain and fault category is not fully utilized, which prevents the transferability of fault features across machines. Towards these issues, a novel ConvFormer-based biconditional domain adaptation method (CFBDAM) is proposed to explore informative and highly-transferable fault features for accurate diagnosis. The proposed ConvFormer network first extracts global-local fault features in a parallel manner via a linear transformer and a separable shuffled CNN, respectively. The resulting features are then fed into a cross-attention feature fusion module to form informative diagnostic knowledge. Our ConvFormer is deployment-friendly owing to lightweight designs, such as linear and separation operations. To enhance cross-machine transferability of the informative fault features extracted by ConvFormer, a biconditional domain adaptation strategy is designed. It imposes biconditional constraints by using the information of both machine domain and fault category, thereby leading to highly-transferable fault features with domain insensitivity and category discriminability. Comprehensive experiments are conducted on six transfer diagnosis tasks across three machines. The experimental results show that CFBDAM achieves potential cross-machine diagnostic performance.