A Simple but Effective Way to Handle Rotating Machine Fault Diagnosis With Imbalanced-Class Data: Repetitive Learning Using an Advanced Domain Adaptation Model

Fault data from in-service rotating machines are extremely scarce. This is usually true even when healthy data are abundant, leading to the problem of class imbalance. Numerous solutions have been proposed to cope with the problem of class imbalance; each solution has its own advantages and disadvantages in implementation. This paper proposes a much simpler and efficient method for fault diagnosis of rotating machines. By employing pseudo-labeling, weighted random sampling, and time-shifting, the proposed repetitive learning method generates pseudo-augmented source and target fault data. Deep convolutional domain adaptation networks are followed to extract features by minimizing different losses. The evaluation results demonstrate the effectiveness of the proposed method, achieving accuracy rates of 90.79% (CWRU), 76.26% (XJTU), and 86.45% (GIST) under extreme imbalance conditions ( rho = 0.01 ), outperforming existing methods by 10-30% while maintaining computational efficiency. The evaluation results show that repetitive learning produces accurate prediction performance even in situations with extremely imbalanced data, which corroborates the effectiveness offered by the proposed method, despite its simplicity.