标题:Vibration feature extraction using local temporal self-similarity for rolling bearing fault diagnosis
作者:Zeng, Shichen ;Lu, Guoliang ;Yan, Peng
作者机构:[Zeng, Shichen ;Lu, Guoliang ;Yan, Peng ] Key Laboratory of High Efficiency and Clean Mechanical Manufacturing of MOE, National Demonstration Center f 更多
会议名称:2019 IEEE International Conference on Prognostics and Health Management, ICPHM 2019
会议日期:June 17, 2019 - June 20, 2019
来源:2019 IEEE International Conference on Prognostics and Health Management, ICPHM 2019
出版年:2019
DOI:10.1109/ICPHM.2019.8819380
摘要:—This paper presents a new method for rolling bearing fault diagnosis. The novel vibration feature extraction is learned with local temporal self-similarities (TSS) continuously from collected vibration signals. The bag-of-words (BoW) scheme is then employed for fault classification taking advantages of these features. We investigated the effectiveness of the framework on the publicly-available Case Western Reserve University (CWRU) data set. We also compare the method with state-of-the-art approaches. The result demonstrates excellent performance of the proposed method, outperforming those compared state-of-the-art approaches.
© 2019 IEEE.
收录类别:EI
资源类型:会议论文
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