LMD energy moment and variable predictive model based class discriminate and their application in intelligent fault diagnosis of roller bearing
Cheng Jun-shen · 2013
Variable predictive model based class discriminate(VPMCD)is a new pattern recognition approach,which takes full advantage of the inhere relation between the feature value.The conception of LMD energy moment was presented in this paper. Aimed at the inhere relation between the feature value of roller bearing,combing the LMD energy moment and VPMCD,a novel intelligent fault diagnosis method was proposed.Firstly,the complicated non-stationary original vibration signal was decomposed into a set of product function components.Secondly,correlation analysis method was used to remove pseudo-components and the energy moment of real PF components with signal feature was extracted as eigenvector to express the fault information adequately.Lastly,VPMCD was served as the approach of pattern recognition to identify roller bearing fault type.The simulation results demonstrate the energy moment of PF components can reflect essential feature of non-stationary signal.The analysis results from practical roller bearings fault vibration signal show that the proposed method can be applied to small sample multiple classification intelligent fault diagnosis of roller bearing effectively.