tailieunhanh - Báo cáo hóa học: " Research Article A Fault Diagnosis Approach for Gears Based on IMF AR Model and SVM"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article A Fault Diagnosis Approach for Gears Based on IMF AR Model and SVM | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 647135 7 pages doi 2008 647135 Research Article A Fault Diagnosis Approach for Gears Based on IMF AR Model and sVm Junsheng Cheng Dejie Yu and Yu Yang The State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha 410082 China Correspondence should be addressed to Junsheng Cheng signalp@ Received 24 July 2007 Revised 28 February 2008 Accepted 15 April 2008 Recommended by Nii Attoh-Okine An accurate autoregressive AR model can reflect the characteristics of a dynamic system based on which the fault feature of gear vibration signal can be extracted without constructing mathematical model and studying the fault mechanism of gear vibration system which are experienced by the time-frequency analysis methods. However AR model can only be applied to stationary signals while the gear fault vibration signals usually present nonstationary characteristics. Therefore empirical mode decomposition EMD which can decompose the vibration signal into a finite number of intrinsic mode functions IMFs is introduced into feature extraction of gear vibration signals as a preprocessor before AR models are generated. On the other hand by targeting the difficulties of obtaining sufficient fault samples in practice support vector machine SVM is introduced into gear fault pattern recognition. In the proposed method in this paper firstly vibration signals are decomposed into a finite number of intrinsic mode functions then the AR model of each IMF component is established finally the corresponding autoregressive parameters and the variance of remnant are regarded as the fault characteristic vectors and used as input parameters of SVM classifier to classify the working condition of gears. The experimental analysis results show that the proposed approach in which IMF AR model and SVM are combined can identify working condition of gears .