The intelligent diagnosis model research of QGA-VPMCD

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Journal of Vibration and Shock ›› 2015, Vol. 34 ›› Issue (13) : 31-35.

YNAG Yu LI Zi-zhu He Zhi-yi CHENG Jun-sheng

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Abstract

Aiming at the defects that only one single model was selected during the variable predictive model-based class discriminate (VPMCD) classification method, an intelligent diagnosis model quantum genetic algorithm - variable predictive model-based class discriminate (QGA-VPMCD) is presented. The optimal weight matrix is used to comprehensively consider the effect of each diagnosis model of classification results in this model. Firstly, multiple subordinate variable predictive models (SVPMs) can be established through samples training. Secondly, intelligent quantum genetic algorithm is used to acquire the weights of each SVPM and the optimal weight matrix is obtained. Finally, optimal weights matrix is exploited to get optimal feature variables predictions by weight fusion for the values, which are predicted by the SVPMs for the test samples, and fault types are identified according to the minimum error square sum which is regarded as discrimination function simultaneously. The analysis results from the vibration signals of rolling bearings show the effectiveness of the proposed model.

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Variable predictive model / quantum genetic algorithm / optimal weight matrix / intelligent diagnosis model

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YNAG Yu LI Zi-zhu He Zhi-yi CHENG Jun-sheng. The intelligent diagnosis model research of QGA-VPMCD[J]. Journal of Vibration and Shock, 2015, 34(13): 31-35

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