A fault mechanism-guided interpretable causal disentanglement domain generalization detection method for typical faults of induction motor

Abstract

Induction motors are widely used in the industrial field such as electric drive systems for new energy vehicles and synchronous condenser for improving the power factor of the power grid. The motor health condition often influences the operation of the entire mechanical system, so it is necessary to conduct a health assessment on it. Current induction motor fault diagnosis largely relies on expert knowledge, while many deep learning methods suffer from limited generalization and poor interpretability, leading to unreliable results. To address these issues, a fault mechanism-guided interpretable causal disentanglement domain generalization detection method (ICGN) is proposed for typical fault diagnosis of induction motor. Firstly, a primary feature extractor is constructed based on transformer, which adaptively screens causal and non-causal factors through the self-attention mechanism, and an attention score evaluation mechanism is constructed to visually demonstrate interpretability. Secondly, to further disentangle and refine causal features and non-causal features, the developed causal aggregation loss and causal decoupling loss are combined, ensuring the cross-domain consistency of causal factors and promote the domain generalization ability of the network. Finally, the proposed method is validated using vibration signals collected from two Spectra Quest test benches from University of Ottawa and the private laboratory. The cases of cross device motor fault diagnosis are included, and the ICGN is compared with several advanced domain generalization algorithms. The results demonstrate that the proposed method achieves superior performance both in interpretability and domain generalization capability.