High-temperature augmented neighborhood metric learning for cross-domain fault diagnosis with imbalanced data

Abstract

The class imbalance in the machine monitoring data and the data distribution discrepancy due to the different working conditions make it difficult to build a strong mechanical fault diagnosis model in practice. In this paper, we proposed a high-temperature augmented neighborhood metric-learning network (HNMN) for cross-domain fault diagnosis with imbalanced data. In the proposed network, fault types are classified by comparing the distance from the sample to the prototype. And neighborhood component analysis-based domain adaptation scheme was presented to reduce the distance of similar samples in source and target domains thus eliminating the domain shift and solving the variable working condition problem. A classification boundary optimization strategy based on high-temperature mechanism was applied to avoid the overlap of feature distributions of minor classes and major classes, and finally solve the class imbalance problem. The performance and superiority of HNMN are evaluated and analyzed using three fault diagnosis cases. The experimental results demonstrate that the proposed network can achieve higher fault diagnosis accuracy compared to the state-of-the-art methods in the class imbalance and variable working condition diagnosis scenarios.