Pattern recognition of rolling bearing fault under multiple conditions based on ensemble empirical mode decomposition and singular value decomposition

Pattern recognition of rolling bearing fault under multiple conditions based on ensemble... In rotating machinery, the malfunctions of rolling bearings are one of the most common faults. To prevent machine breakdown, the pattern recognition of rolling bearing faults has been a pivotal issue for fault identification and classification. This study proposes a new feature extraction method based on ensemble empirical mode decomposition (EEMD) and singular value decomposition (SVD) for fault classification. The proposed E–S method (EEMD combined with SVD using feature parameters) intends to enhance the faults identification capability in different working conditions, including various fault types (FT), fault severities (FS), and fault loads (FL). In this study, the E–S method is adopted to analyze the simulated signals. And the experiment further discusses three cases of different FT, FS, and FL separately under six different classifiers. The experimental results show that different fault classes can be effectively distinguished by the proposed E–S in comparison with other traditional feature extraction methods. Hence, the proposed method is verified to have an effective and excellent performance in bearing fault classification. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science SAGE

Pattern recognition of rolling bearing fault under multiple conditions based on ensemble empirical mode decomposition and singular value decomposition

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Publisher
SAGE Publications
Copyright
© IMechE 2017
ISSN
0954-4062
eISSN
2041-2983
D.O.I.
10.1177/0954406217715483
Publisher site
See Article on Publisher Site

Abstract

In rotating machinery, the malfunctions of rolling bearings are one of the most common faults. To prevent machine breakdown, the pattern recognition of rolling bearing faults has been a pivotal issue for fault identification and classification. This study proposes a new feature extraction method based on ensemble empirical mode decomposition (EEMD) and singular value decomposition (SVD) for fault classification. The proposed E–S method (EEMD combined with SVD using feature parameters) intends to enhance the faults identification capability in different working conditions, including various fault types (FT), fault severities (FS), and fault loads (FL). In this study, the E–S method is adopted to analyze the simulated signals. And the experiment further discusses three cases of different FT, FS, and FL separately under six different classifiers. The experimental results show that different fault classes can be effectively distinguished by the proposed E–S in comparison with other traditional feature extraction methods. Hence, the proposed method is verified to have an effective and excellent performance in bearing fault classification.

Journal

Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering ScienceSAGE

Published: Jun 1, 2018

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