Rolling bearing fault diagnosis method based on permutation entropy and VPMCD

被引：0
|
作者：
机构：
[1] State Key Laboratory of Advanced Design and Manufacture for Vehicle Body, Hunan University
来源：
Cheng, J.-S. | 1600年 / Chinese Vibration Engineering Society卷 / 33期
关键词：
Fault diagnosis; Intrinsic time-scale decomposition (ITD); Permutation entropy; Rolling bearing; Variable predictive model-based class discriminate (VPMCD);
D O I：
10.13465/j.cnki.jvs.2014.11.021
中图分类号：
学科分类号：
摘要：
Variable predictive model-based class discriminate (VPMCD) is a new pattern recognition method, it makes full use of inner relations among characteristic values extracted from the original data to recognize models. Here, VPMCD was combined with permutation entropy (PE) to diagnose rolling bear faults. Firstly, rolling bearing vibration signals were adaptively decomposed into a sum of proper rotation (PR) components by using ITD and the permutation entropies of PR components containing the main faults information were extracted as characteristic values of faults. Secondly, the characteristic values were used to train the parameters of VPMCD. Finally, the VPMCD classifier was used to recognize and classify the faults. The experimental results showed that this method can be effectively applied to diagnose rolling bearing faults.
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页码：119 / 123
页数：4
相关论文
共 19 条
  • [1] (2007)
  • [2] Lei Y.-G., He Z.-J., Zi Y.-Y., Et al., New clustering algorithm-based fault diagnosis using compensation distance evaluation technique, Mechanical Systems and Signal Processing, 22, 2, pp. 419-435, (2008)
  • [3] Rafiee J., Tse P.W., Harifi A., Et al., A novel technique for selecting mother wavelet function using an intelligent fault diagnosis system, Expert System with Applications, 36, 3, pp. 4862-4875, (2009)
  • [4] Wang C.C., Kang Y., Shen P.C., Et al., Applications of fault diagnosis in rotating machinery by using time series analysis with neural network, Expert System With Application, 37, 2, pp. 1696-1702, (2010)
  • [5] Fei S.-W., Zhang X.-B., Fault diagnosis of power transformer based on support vector machine with genetic algorithm, Expert Systems with Application, 36, 8, pp. 11352-11357, (2009)
  • [6] Raghuraj R., Lakshminarayanan S., Variable predictive models-A new multivariate classification approach for pattern recognition applications, Pattern Recognition, 42, 1, pp. 7-16, (2009)
  • [7] Raghuraj R., Lakshminarayanan S., Variable predictive model based classification algorithm for effective separation of protein structural classes, Computational Biology and Chemistry, 32, 4, pp. 302-306, (2008)
  • [8] Raghuraj R., Lakshminarayanan S., VPMCD: Variable interaction modeling approach for class discrimination in biological system, FEBS Letter, 581, 5, pp. 826-830, (2007)
  • [9] Wang X.Y., Makis V., Yang M., A wavelet approach to fault diagnosis of a gearbox under varying load conditions, Journal of Sound and Vibration, 329, 9, pp. 1570-1585, (2010)
  • [10] Kankar P.K., Sharma S.C., Harsha S.P., Fault diagnosis of ball bearing using continuous wavelet transform, Applied Soft Computing, 11, 2, pp. 2300-2312, (2011)
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