A fault diagnosis approach for roller bearing based on improved intrinsic timescale decomposition de-noising and kriging-variable predictive model-based class discriminate

The measured vibration signal of roller bearings has noise signals, which will largely influence the accuracy of roller bearing fault diagnosis. This paper puts forward a vibration signal de-noising method based upon improved intrinsic timescale decomposition (ITD); fuzzy entropy is then extracted as the fault feature of the roller bearing. Essentially the fault diagnosis of roller bearings is a process of pattern recognition. Targeting the limitation of existing pattern recognition methods, a new pattern recognition method – variable predictive model-based class discriminate (VPMCD) – is introduced into roller bearing fault identification. In the original VPMCD classifier, however, only the regression model could be used to predict, which will reduce the prediction’s accuracy when the relation between features is complicated. Aimed at this defect, a kriging-variable predictive model-based class discriminate (KVPMCD) pattern recognition method is presented in this paper. The kriging model is composed of a regression model and a correlation model, of which the correlation model is a local deviation that is created on the basis of a global model, to make up for the shortcomings of the simple regression model in the original VPMCD. Therefore, a fault diagnosis approach for roller bearing based on improved ITD de-nosing and KVPMCD is proposed in this paper. The analysis results from experimental signals with normal and defective roller bearings indicate that the proposed fault diagnosis approach can accurately and effectively distinguish the status of roller bearings with or without fault and fault patterns.

[1]  I. Johnstone,et al.  Adapting to Unknown Smoothness via Wavelet Shrinkage , 1995 .

[2]  Jingjing Liu,et al.  A novel fault analysis and diagnosis method based on combining computational intelligence methods , 2013 .

[3]  Li Jiang,et al.  Fault diagnosis of rolling bearings based on Marginal Fisher analysis , 2014 .

[4]  Gou Peng.  A Comparison of Approximation Methods for Multidisciplinary Design Optimization of Ship Structures , 2007 .

[5]  V. Sugumaran,et al.  Fault diagnosis of roller bearing using fuzzy classifier and histogram features with focus on automatic rule learning , 2011, Expert Syst. Appl..

[6]  Yaguo Lei,et al.  Application of the EEMD method to rotor fault diagnosis of rotating machinery , 2009 .

[7]  Sheng-wei Fei,et al.  Fault diagnosis of power transformer based on support vector machine with genetic algorithm , 2009, Expert Syst. Appl..

[8]  Jianzhong Zhou,et al.  Fault diagnosis based on Walsh transform and rough sets , 2009 .

[9]  Joshua R. Smith,et al.  The local mean decomposition and its application to EEG perception data , 2005, Journal of The Royal Society Interface.

[10]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[11]  N. Cressie.  The origins of kriging , 1990 .

[12]  Jinde Zheng,et al.  Generalized empirical mode decomposition and its applications to rolling element bearing fault diagnosis , 2013 .

[13]  Lei Guo,et al.  Rolling Bearing Fault Classification Based on Envelope Spectrum and Support Vector Machine , 2009 .

[14]  YU De-jie.  Comparison between the methods of local mean decomposition and empirical mode decomposition , 2009 .

[15]  Gabriel Rilling,et al.  On empirical mode decomposition and its algorithms , 2003 .

[16]  Jian-Da Wu,et al.  An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network , 2009, Expert Syst. Appl..

[17]  N. Huang,et al.  A new view of nonlinear water waves: the Hilbert spectrum , 1999 .

[18]  Ming Liang,et al.  Application of multi-scale chirplet path pursuit and fractional Fourier transform for gear fault detection in speed up and speed-down processes , 2012 .

[19]  Tielin Shi,et al.  A novel fault diagnosis method of bearing based on improved fuzzy ARTMAP and modified distance discriminant technique , 2009, Expert Syst. Appl..

[20]  Yaguo Lei,et al.  EEMD method and WNN for fault diagnosis of locomotive roller bearings , 2011, Expert Syst. Appl..

[21]  N. R. Sakthivel,et al.  Multi component fault diagnosis of rotational mechanical system based on decision tree and support vector machine , 2011, Expert Syst. Appl..

[22]  Wangxin Yu,et al.  Characterization of Surface EMG Signal Based on Fuzzy Entropy , 2007, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[23]  Niaoqing Hu,et al.  Stochastic resonance in multi-scale bistable array , 2013 .

[24]  S. S. Shen,et al.  A confidence limit for the empirical mode decomposition and Hilbert spectral analysis , 2003, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[25]  Cheng Junsheng,et al.  Research on the intrinsic mode function (IMF) criterion in EMD method , 2006 .

[26]  I. Osorio,et al.  Intrinsic time-scale decomposition: time–frequency–energy analysis and real-time filtering of non-stationary signals , 2007, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[27]  Samavedham Lakshminarayanan,et al.  Variable predictive models - A new multivariate classification approach for pattern recognition applications , 2009, Pattern Recognit..

[28]  Junsheng Cheng,et al.  Application of support vector regression machines to the processing of end effects of Hilbert Huang transform , 2007 .

[29]  A. Papritz,et al.  An Empirical Comparison of Kriging Methods for Nonlinear Spatial Point Prediction , 2002 .

[30]  Huaqing Wang,et al.  Intelligent diagnosis method for rolling element bearing faults using possibility theory and neural network , 2011, Comput. Ind. Eng..