Counterfactual Data Generation Method for Fault Diagnosis of Complex Electromechanical Systems

The high reliability and maintainability of complex electromechanical systems make the collection of representative fault data difficult in routine operations. This issue poses significant challenges to data-driven models, hindering their ability to accurately capture system fault mechanisms. Generating simulated fault samples is a popular and effective approach to augment fault data. However, state-of-the-art data generation methods can only produce synthetic data with respect to the distribution of the collected data and may violate the actual fault mechanisms. To address this, this article proposes a counterfactual data generation method grounded in causality, aiming to simulate the intrinsic generation process of monitoring data and thereby obtain counterfactual data that conforms to the system’s fault mechanisms. In the proposed method, a priori-constrained causal discovery method is designed to uncover the causalities among the monitoring variables in complex electromechanical systems. A graph decoupling network is designed to disentangle causal mechanisms and extract decoupled features representing different uncertainty sources of the monitoring data. Finally, a causal-based generative adversarial network (CGAN) is proposed to generate counterfactual monitoring data that satisfies the mined causalities. The experimental results show that the generalizability and stability of fault diagnosis models can be enhanced with the counterfactual data.