Waveform-Prediction Augmentation and Deep Manifold Learning Enable Imbalanced Fault Diagnosis in Rotating Machinery

Abstract

Fault diagnosis of rotating machinery is essential for ensuring the reliable and safe operation of industrial equipment. However, imbalanced training data often bias intelligent diagnostic models toward majority classes, resulting in insufficient representation of minority faults. In addition, the high dimensionality and redundancy of vibration signals make it difficult to extract discriminative features and preserve meaningful neighborhood structures, thereby degrading diagnostic performance. To address these challenges, a waveform-prediction augmentation and deep manifold learning model for imbalanced fault diagnosis is proposed for rotating machinery. First, minority-class fault signals are augmented through time-series prediction using a nonlinear autoregressive neural network (NARNN), thereby alleviating the class imbalance at the data level. Subsequently, a deep manifold feature mapping (DMAP) is proposed to extract discriminative fault features. In DMAP, a stacked autoencoder (SAE) is employed to perform preliminary feature extraction on the training samples and obtain deep latent feature representations. The deep features extracted by the SAE are further processed using a dynamic time warping (DTW)-assisted manifold learning method for dimensionality reduction. In this process, DTW is used to calculate pairwise dissimilarities between deep representations and construct the neighborhood graph, thereby better preserving the local neighborhood relationships and topological structure of the deep features and producing more discriminative low-dimensional representations. Finally, a k-nearest neighbor classifier is adopted for fault classification. By integrating data augmentation, deep feature learning, and manifold dimensionality reduction, the proposed model effectively improves the fault diagnosis performance in imbalanced scenarios. Experiments on the bearing and planetary gearbox datasets demonstrate that, under an imbalance ratio of 0.1, the proposed method improves diagnostic accuracy by 10.2 and 1.6 percentage points, respectively. The comparative results further confirm its effectiveness in alleviating the diagnostic difficulties caused by class imbalance.