期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 37, 期 2, 页码 1419-1430出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2009.06.060
关键词
Feature extraction; Hybrid intelligent method; Classifier combination; Gear fault diagnosis
类别
资金
- Natural Sciences and Engineering Research Council of Canada (NSERC)
Identifying gear damage categories. especially for early faults and combined faults. is a challenging task in gear fault diagnosis This paper proposes a new multidimensional hybrid intelligent diagnosis method to identify different categories and levels of gear damage automatically. In this method, Hilbert transform. wavelet packet transform (WPT) and empirical mode decomposition (EMD) are performed on gear vibration signals to extract additional fault characteristic information Then. multidimensional feature sets including time-domain, frequency-domain and time-frequency-domain features are generated to reveal gear health conditions. Multiple classifiers based oil several classification algorithms and input features are combined with genetic algorithm (GA). Because of the use of multidimensional features and the combination of multiple classifiers. more accurate diagnosis results are expected with the proposed method. Experiments with different gear damage categories and damage levels were conducted. and the vibration signals were captured under different loads and motor speeds. The proposed method is applied to the collected signals to identify the gear damage categories and damage levels. The diagnosis results show it can reliably recognize single damage modes. combined damage modes, and damage levels (C) 2009 Elsevier Ltd All rights reserved.
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