Fault Diagnosis for Imbalanced Datasets Based on Deep Convolution Fuzzy System

To address the data imbalance issue in the process of collecting bearing fault data in industrial environments and to enhance the robustness and generalization ability of fault diagnosis, this paper proposes a bearing fault diagnosis method based on a Bidirectional Autoregressive Variational Autoencoder (BAVAE) and a Deep Convolutional Interval Type-2 Fuzzy System (DCIT2FS). First, the method extracts features from the imbalanced dataset using dual-tree complex wavelet transform (DTCWT), and then feeds the feature dataset into the proposed BAVAE for data augmentation. The BAVAE improves data generation capabilities by introducing autoregressive distributions to learn latent variables, iteratively obtaining complex high-order latent variables, and amplifying inter-class differences through the introduction of feature discrimination loss during training. Given that relying solely on data augmentation under imbalanced data conditions may lead to overfitting or underfitting, this paper combines the generalization approximation ability of Interval Type-2 (IT2) fuzzy systems with the feature extraction capability of deep convolutional networks, achieving a better balance between model complexity and feature transformation, thereby enhancing the stability and accuracy of the final diagnosis.