Imbalanced data fault diagnosis of rotating machinery using synthetic oversampling and feature learning

Imbalanced data problems are prevalent in the real rotating machinery applications. Traditional data-driven diagnosis methods fail to identify the fault condition effectively for lack of enough fault samples. Therefore, this study proposes an effective three-stage fault diagnosis method towards imbalanced data. First, a new synthetic oversampling approach called weighted minority oversampling (WMO) is devised to balance the data distribution. It adopts a new data synthesis strategy to avoid generating incorrect or unnecessary samples. Second, to select useful features automatically, an enhanced deep auto-encoder (DA) approach is adopted. DA is improved in two aspects: 1) a new cost function based on maximum correntropy and sparse penalty is designed to learn sparse robust features; 2) a fine-tuning operation with a self-adaptive learning rate is developed to ensure the good convergence performance. Finally, the C4.5 decision tree identifies the learned features. The proposed method named WMODA is evaluated on 25 benchmark imbalanced datasets. It achieves better results than five well-known imbalanced data learning methods. It is also evaluated on a real engineering dataset. The experimental results show that WMODA can detect more fault samples than the traditional data-driven methods.