Domain expansion fusion single-domain generalization framework for mechanical fault diagnosis under unknown working conditions

Abstract

In real industrial scenarios, mechanical systems often adjust working conditions based on specific tasks, leading to challenges in collecting data for all possible machine states in advance. Consequently, applying deep learning models trained on data from a single working condition directly to other unknown working condition poses a significant challenge. To tackle this issue, a novel domain expansion fusion single-domain generalization framework is proposed for machinery fault diagnosis under unknown working conditions. Firstly, a domain expansion module that can be controlled via a constraint function is developed to create expanded domains that generate samples with controlled differences from the source domain. Subsequently, a dual-branch feature fusion network is proposed in the feature extraction module. It combines two distinct feature extractors and employs a weighted average fusion strategy to extract discriminative features. Additionally, a state recognition module is implemented through a multi-classifier ensemble strategy to enhance the robustness and accuracy of health state identification. Lastly, an adversarial contrastive training strategy is employed to optimize the network and enhance its generalization capabilities and fault diagnosis performance. Through case studies conducted on two mechanical fault datasets, the proposed method demonstrates good diagnosis performance on single-domain generalized diagnosis tasks with an average accuracy of 87.53%. Its generalization effect is validated. Furthermore, the comparison and ablation experiment results confirm the effectiveness and superior performance of the proposed intelligent fault diagnosis method in scenarios with unknown working conditions with an average accuracy improvement of at least 3.94%.