Intelligent Bearing Anomaly Detection for Industrial Internet of Things Based on Auto-Encoder Wasserstein Generative Adversarial Network

Bearing anomaly detection plays a crucial role in modern industries as most rotating machinery faults are attributed to faulty bearings. However, acquiring fault samples in industry is a time-consuming and expensive process. To address this issue, this paper presents an integrated unsupervised learning method named AE-AnoWGAN (Autoencoder Wasserstein Generative Adversarial Network). AE-AnoWGAN is capable of detecting abnormal bearings and performing anomaly localization without the need for labeled data. In this approach, industrial data is initially processed using continuous wavelet transform to convert it into time-frequency representations (TFRs). These TFRs are then fed into the integrated AE-AnoWGAN for training. AE-AnoWGAN consists of multiple encoder-decoder and discriminator pairs, which are randomly paired and trained using adversarial training. The encoder maps the TFRs to a latent space, and the pre-trained generator acts as the decoder to generate reconstructed TFRs. During the testing phase, the model calculates anomaly scores for the input TFRs. Experimental evaluations were conducted using the PU bearing dataset and IMS bearing dataset. Comparative results demonstrate that the proposed AE-AnoWGAN method outperforms existing approaches in terms of anomaly detection accuracy. Moreover, the method exhibits high anomaly detection efficiency, making it suitable for real-time monitoring applications. Furthermore, this method provides practical value by enabling anomaly localization and bearing degradation estimation of TFRs.