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• W4366967784 abstract "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." @default.
• W4366967784 created "2023-04-26" @default.
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• W4366967784 date "2022-09-11" @default.
• W4366967784 modified "2023-09-26" @default.
• W4366967784 title "Anomaly Detection based on Broad Leaning System for Rolling Element Bearing Fault Diagnosis" @default.
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• W4366967784 doi "https://doi.org/10.1145/3544793.3560408" @default.
• W4366967784 hasPublicationYear "2022" @default.
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