《机床与液压》 Hydromechatronics Engineering

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稿件标题: Research on Diagnosis Method of Elevator Fault Based on LS- SVM
稿件作者: ZHENG Jian-jun, CHEN Zhi-jun, DU Ke-feng
关键字词: fault diagnosis, feature extracting, least square support vector machine (LS-SVM), wavelet packet, elevator
文章摘要: Aimed at the puzzle of being difficult to extract the fault features and less in fault data sample, the paper proposed a sort of diagnosis method of elevator fault based on least squaree support vector machine(LS- SVM). By means of wavelet packet analysis and LS-SVM, it firstly extracted the elevator fault data samples by wavelet packet analysis, and then identified the elavator fault based on their characteristics by use of LS-SVM. The test data of actual experiment demonstrsted that it has better effect in diagnosis for jerk fault by the proposed method. The research result shows that it is excellent in diagnosis performance.
收录刊物: 2012.12
稿件基金: Key projects of Department of Education of Xinjiang Uygur Autonomous Region of China (ID: XJEDU2010I07); Ministry of Science and Technology Innovation Fund of China (ID: 11C26216506453).
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