Causal Consistency Network: A Collaborative Multimachine Generalization Method for Bearing Fault Diagnosis

Due to the lack of faulty data on the target machine, intelligent networks often need to learn fault knowledge from other relevant machines. Unfortunately, data from different machines introduce individualized deviations, which may lead to overfitting of network learning and reduce its generalization ability. To address the problem, this article proposes a collaborative multimachine generalization method—causal consistency network (CCN), which mines the invariant causal information in individualized machines through a collaborative way to achieve knowledge generalization. In CCN, instead of emphasizing the domain invariance of features, causal consistency loss depicting the consistency of fault causality representations in deep latent variables is proposed. Moreover, to transform the individualized data of different machines into consistent representations, a collaborative training loss is proposed to describe the underlying invariant causal mechanism of the fault features. The generalization results among 6 machines containing 43 individual bearings and 20 operating conditions demonstrate the superiority of CCN.