CIRNet: An Interpretable Cross-Component Few-Shot Mechanical Fault Diagnosis

Abstract

In recent years, several few-shot learning (FSL) approaches for industrial equipment fault diagnosis have emerged to tackle the challenges posed by small fault diagnosis datasets. However, the existing FSL approaches model the correlation between input and output variables while ignoring causality, which cannot ensure that the diagnosis results are interpretable and robust. To tackle this problem, this article introduces a causal intervention relation network for cross-component few-shot fault diagnosis from the causal perspective. The model comprises a feature encoding module, a causal intervention module, and a relation measure module. The feature encoding module and the relation measure module establish a trainable similarity metric space through the training of multiple metatasks, where they learn the feature distances between sample pairs. Importantly, in causal intervention module, we model the causal structure of the metalearning process of few-shot fault diagnosis to find the causal fault features and the confounder factor, i.e., the metatraining diagnosis knowledge. Correspondingly a backdoor adjustment approach via a combination of class-based adjustment and feature adjustment is designed to realize the causal calibration of the few-shot fault diagnosis model. In such way, the model can capture causal invariant features between various components with significant distributional differences, thus enhancing the model's interpretability and its capacity for generalization. We perform experiments on two openly accessible datasets and a dataset constructed in our laboratory. The experimental results demonstrate that the model outperforms existing state-of-the-art approaches.