Machine Fault Diagnosis Method Based on ITD and Variable Predictive Model Based Class Discriminate

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Journal of Vibration and Shock ›› 2013, Vol. 32 ›› Issue (13) : 43-48.

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Machine Fault Diagnosis Method Based on ITD and Variable Predictive Model Based Class Discriminate

LUO Song-rong1,2, CHENG Jun-sheng1, YANG Yu1

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Abstract

Variable predictive model based class discriminate (VPMCD) is a new multivariate classification approach for pattern recognition applications, which takes full advantage of the inhere relation between the features .The generalized VPMCD algorithm was studied and the VPMCD model was selected via cross validation method in the paper. In view of inter-relations among the feature vectors of machine fault vibration signal, a new machine failure diagnosis method based on ITD (intrinsic time-scale decomposition, ITD) and VPMCD was proposed with the combination of the advantages of ITD method. Firstly, non-stationary original signal was decomposed into a set of proper rotation components. Secondly，five traditional time domain dimensionless parameters of first proper rotation component were extracted as eigenvectors .Lastly, VPMCD was exploited to identify fault type . It is demonstrated that the proposed method can be effectively applied to diagnose machine failure in case of small sample multivariate classification by fault diagnosis experiment of roller bearing.

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intrinsic time-scale decomposition / variable predictive model / multiple classification / machine fault diagnosis / machine learning

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LUO Song-rong;CHENG Jun-sheng;YANG Yu. Machine Fault Diagnosis Method Based on ITD and Variable Predictive Model Based Class Discriminate [J]. Journal of Vibration and Shock, 2013, 32(13): 43-48