HAGAN: A Lightweight Compressed Sensing Framework for Motor Bearing Fault Diagnosis

Abstract

Motor bearings are the most failure-prone components in industrial motors, and the accuracy of their fault diagnosis is highly dependent on the effective acquisition and analysis of vibration signals. However, traditional Compressed Sensing (CS) methods face an inherent trade-off between compression ratio and reconstruction accuracy, while deep learning-enhanced CS models generally suffer from complex architectures and insufficient real-time performance. These drawbacks severely restrict their practical application in industrial fault diagnosis scenarios. To address the aforementioned challenges, this study proposes a lightweight compressed sensing framework tailored for motor bearing fault diagnosis-Hybrid Autoencoder Generative Adversarial Network (HAGAN). This framework integrates the core advantages of Autoencoders (AE) and Generative Adversarial Networks (GAN). The AE is responsible for extracting key fault features and compressing high-dimensional vibration signals, while the GAN improves the fidelity of reconstructed signals through an adversarial training mechanism. Meanwhile, a streamlined network structure design is adopted to remove redundant nonlinear modules, thereby reducing computational overhead and ensuring real-time deployment capabilities in industrial settings. In the experiments, this method achieves an ultra-high data compression ratio of 100:1 for motor bearing fault data. Three metrics-Root Mean Square Error (RMSE), Percentage Root Mean Square Deviation (PRD), and Signal-to-Noise Ratio (SNR)-are employed to comprehensively verify the compression and reconstruction performance. The results demonstrate that the HAGAN framework can effectively preserve the characteristic information of bearings in both healthy states and various fault states (including inner race faults, outer race faults, rolling element faults, and combination faults) even under high compression ratios. Its compression and reconstruction performance, quantified and validated by the three metrics, is excellent. The framework can be successfully applied to data compression and reconstruction tasks in motor bearing fault diagnosis, providing an efficient and reliable fault diagnosis solution for resource-constrained industrial environments.