Anomaly Detection based on Broad Leaning System for Rolling Element Bearing Fault Diagnosis

The latest proposed Broad Learning System (BLS) demonstrates an efficient and effective learning capability in many machine learning problems. In this paper, we develop a BLS based anomaly detection method (AD-BLS) for rolling element bearing fault diagnosis. Our method applies wavelet packet transformation (WPT) to extract the energy features, which will be further fed to the proposed BLS-based model to learn the intrinsic representations for the normal states of rolling bearings to conduct anomaly detection. Benefiting from the WPT pre-process, AD-BLS can be effectively trained with a small size of data. Moreover, due to the online updating mechanism of BLS, the designed AD-BLS can be easily updated with new extra data without re-design or retraining from scratch, which largely improves the flexibility of our method in real-world applications. Experimental results on the rolling element bearing fault diagnosis dataset further demonstrate the effectiveness of our method.