Intelligent Bearing Fault Diagnosis Under Various Load Conditions Using Bias Mitigation

Intelligent bearing fault diagnosis with domain adaptations has accomplished remarkable performances under various operating conditions. However, especially for different load conditions, the model bias due to the physical characteristics of bearing signals has not been considered. In the absence of handling bias, the root cause for generalization errors cannot be clarified under various load conditions. This paper thus demonstrates that certain bias exists in diagnostic models for different loads of bearings, and the main factor of bias is impulsiveness. The existence of bias is shown with quantitative analysis by applying fairness criteria to diagnostic models. Also, qualitative analysis is conducted with gradient-weighted class activation mapping (Grad-CAM) for vibration signals of bearings, which proves that the large amplitude of impulse can be the source of bias. To correct this impulsiveness bias, a framework of fairness approach is newly proposed for bearing fault diagnosis under various loads. The process of correcting bias contains two steps: categorizing samples based on impulsiveness and training models with fairness criteria. Different from the previous domain adaptation-based approaches, the proposed method can achieve superior diagnostic performances by correcting bias that causes generalization errors. The effectiveness of the proposed method is validated with public-bearing datasets with various loads. The results show that the fairness approach can be the mainstream solution for fault diagnosis of rotary machines under different load conditions.