TY - GEN
T1 - Anomaly Detection based on Broad Leaning System for Rolling Element Bearing Fault Diagnosis
AU - Yang, Le
AU - Yang, Zelin
AU - Zheng, Ziwei
AU - He, Lijun
AU - Li, Fan
AU - Chen, C. L.Philip
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2023/4/24
Y1 - 2023/4/24
N2 - 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.
AB - 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.
KW - Anomaly detection
KW - broad learning system
KW - fault diagnosis
KW - single-layer feedfoward neural network (SLFN)
UR - https://www.scopus.com/pages/publications/85158984561
U2 - 10.1145/3544793.3560408
DO - 10.1145/3544793.3560408
M3 - 会议稿件
AN - SCOPUS:85158984561
T3 - UbiComp/ISWC 2022 Adjunct - Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2022 ACM International Symposium on Wearable Computers
SP - 462
EP - 467
BT - UbiComp/ISWC 2022 Adjunct - Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2022 ACM International Symposium on Wearable Computers
PB - Association for Computing Machinery, Inc
T2 - 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2022 ACM International Symposium on Wearable Computers, UbiComp/ISWC 2022
Y2 - 11 September 2022 through 15 September 2022
ER -