Vibration feature extraction using local temporal self-similarity for rolling bearing fault diagnosis

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...

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Bibliographic Details
Published in2019 IEEE International Conference on Prognostics and Health Management (ICPHM) pp. 1 - 5
Main Authors Zeng, Shichen, Lu, Guoliang, Yan, Peng
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2019
Subjects
Fault diagnosis
Feature extraction
Histograms
Support vector machines
Testing
Training
Vibrations
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Summary: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.
DOI:10.1109/ICPHM.2019.8819380