Unsupervised Bearing Fault Diagnosis Using Masked Self-Supervised Learning and Swin Transformer

Bearings are vital to rotating machinery, where undetected faults can cause severe failures. Conventional fault diagnosis methods depend on manual feature engineering and labeled data, struggling with complex industrial conditions. This study introduces an innovative unsupervised framework combining masked self-supervised learning with the Swin Transformer for bearing fault diagnosis. The novel integration leverages masked Auto Encoders to learn robust features from unlabeled vibration signals through reconstruction-based pretraining, while the Swin Transformer’s shifted window attention mechanism enhances efficient capture of fault-related patterns in long-sequence signals. This approach eliminates reliance on labeled data, enabling precise detection of unknown faults. The proposed method achieves 99.53% accuracy on the Paderborn dataset and 100% accuracy on the CWRU dataset significantly, surpassing other unsupervised Auto Encoder-based methods. This method’s innovative design offers high adaptability and substantial potential for predictive maintenance in industrial applications.