Work

Attention-Enhanced Conditional Wasserstein GAN with Wavelet–ResNet for Fault Diagnosis Under Imbalanced Data

Year: 2025

Type: article

Abstract: Rolling bearings are critical components in mechanical systems, and their health directly affects operational reliability and safety. However, their exposure to harsh conditions makes accurate fault diagnosis essential. Conventional methods relying on expert knowledge and handcrafted features are inefficient, while deep learning still suffers from data imbalance, which limits generalization. To address this challenge, an Attention-Enhanced Conditional Wasserstein GAN (ACWGAN) is proposed, in which the attention mechanism is incorporated into both the generator and discriminator to capture global dependencies and enhance feature diversity. By combining attention guidance with the Wasserstein distance, the framework achieves more stable training, alleviates mode collapse, and generates high-fidelity fault samples to balance imbalanced datasets. Compared with existing GAN-based methods, this method, combined with wavelet-based ResNet, significantly improves the accuracy of diagnosis, achieving 100% accuracy in the generated dataset.

Source: Processes

DOI: 10.3390/pr13113531

Authors: Hua Tu, Yuandong Zhang, Xiuli Wang, Yang Li

Institutions: Shanghai University, Zhejiang University of Technology, Nankai University

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funders: National Natural Science Foundation of China, China Postdoctoral Science Foundation