基于ASTFA降噪和AKVPMCD的滚动轴承故障诊断方法

摘要/Abstract

摘要：

提出了一种滚动轴承故障诊断的新方法。首次将自适应最稀疏时频分析(ASTFA)方法应用于振动信号的降噪，并针对KVPMCD方法只选择一种最佳相关模型而忽略其他几种相关模型对预测精度贡献的缺陷，提出了一种改进的KVPMCD模式识别算法——人工鱼群算法优化融合Kriging模型的基于变量预测模型的模式识别（AKVPMCD）算法,即采用收敛速度快、鲁棒性强、具有全局寻优能力的人工鱼群智能算法(AFSIA)优化融合多种Kriging相关模型来提高模型预测精度。在此基础上，提出了一种基于ASTFA降噪和AKVPMCD算法的滚动轴承故障诊断方法。实验结果表明，该方法可以有效提高分类识别的精度。

关键词: 自适应最稀疏时频分析降噪, AKVPMCD, 滚动轴承, 故障诊断

Abstract:

A new rolling bearing fault diagnosis method was proposed. ASTFA method was applied to the vibration signal de-noising for the first time.Aiming at the defects of KVPMCD(Kriging model variable predictive model based class discriminate) method that was chosen one of the best related model only and the other correlation models' contribution to the prediction accuracy was ignored,an improved KVPMCD pattern recognition algorithm AKVPMCD was proposed,the AFSIA(artificial fish swarm intelligence algorithm) which had high convergence speed, strong robustness and global optimization ability was used to optimize a variety of Kriging models,so as to improve the prediction precision. On the basis of above, a new fault diagnosis method of rolling bearings was proposed based on ASTFA de-noising and AKVPMCD. The experimental results prove that this method can improve the precision of classification recognition effectively.

Key words: adaptive and sparsest time-frequency analysis(ASTFA) , de-noising;artificial fish swarm algorithm optimizing fusion Kriging model variable predictive model based class discriminate(AKVPMCD);rolling bearing, fault diagnosis

中图分类号:

TH113

杨宇, 李紫珠, 何知义, 程军圣. 基于ASTFA降噪和AKVPMCD的滚动轴承故障诊断方法[J]. 中国机械工程, 2015, 26(21): 2934-2940.

Yang Yu, Li Zizhu, He Zhiyi, Cheng Junsheng. The Rolling Bearing Fault Diagnosis Method Based on ASTFA De-noising and AKVPMCD[J]. China Mechanical Engineering, 2015, 26(21): 2934-2940.

参考文献

[1]Yi Weiguo,Duan Jing,Lu Mingyu.Double-layer Bayesian Classifier Ensembles Based on Frequent Itemsets[J].International Journal of Automation and Computing,2012,9(2):215-220.
[2]Chen Youshyang,Cheng Chinghsue.Hybrid Models Based on Rough Set Classifiers for Setting Credit Rating Decision Rules in the Global Banking Industry[J].Knowledge-based Systems,2013,39:224-239.
[3]Wang Huaqing,Chen Peng.Intelligent Diagnosis Method for Rolling Element Bearing Faults Using Possibility Theory and Neural Network[J].Computer & Industrial Engineering,2011,60(4):511-518.
[4]Fei Shengwei,Zhang Xiaobin.Fault Diagnosis of Power Transformer Based on Support Vector Machine with Genetic Algorithm[J]. Expert Systems with Applications,2009,36(8):11352-11357.
[5]Raghuraj R,Lakshminarayanan S.Variable Predictive Models—a New Multivariate Classification Approach for Pattern Recognition Applications[J].Pattern Recognition,2009,42(1):7-16.
[6]杨宇,潘海洋,程军圣.基于Kriging函数的KVPMCD在滚动轴承故障诊断中的应用[J].中国机械工程,2014,25(16):2131-2136.
Yang Yu,Pan Haiyang,Chen Junsheng.Application of KVPMCD Based on Kriging Function in Rolling Bearing Fault Diagnosis[J]. China Mechanical Engineering,2014,25(16):2131-2136.
[7]Verma H, Agrawal R   K,Kumar N.Improved Fuzzy   Entropy Clustering Algorithm for MRI   Brain Image Segementation[J]. International Journal of   Imaging Systems and Technology,2014,24(4):277-283.
[8]Sayadi O, Shamsollahi   M   B. ECG Denoising and Compression    Using a Modified Extended Kalman Filter Structure[J]. IEEE   Transaction on Biomedical Enginner,2008,55(9):2240-2248.
[9]Donoho D L,Johnstone   M I.Adapting to Unknown Smoothness via Wavelet Shrinkage[J].Journal of the American Statistical Association,1995,90(12):1200-1224.
[10]Liu Junmin,Zhang Jiangshe.Spectral Unmixing via Compressive Sensing[J].IEEE Transaction on Geoscience and Remote Sensing,2014,52(11):7099-7110.
[11]Hou T Y,Shi Zuoqiang. Adaptive Data Analysis via Sparse Time-frequency Representation[J].Advances in Adaptive Data Analysis,2011,3(1/2):1-28.
[12]Hou T Y.Shi Zuoqiang.Data Driven Time-frequency Analysis[J].Applied and Computational Harmonic Analysis,2013,35(2):284-308.
[13]Bhattacharjee S,Mitra P,Ghosh S K.Spatial Interpolation to   Predict Missing Attributes in GIS Using Semantic Kriging[J].IEEE Transaction on Geoscience and Remote Sensing,2014,52(8):4771-4780.
[14]王联国,施秋红.人工鱼群算法的参数分析[J].计算机工程,2010,36(27):169-171.
Wang Lianguo,Shi Qiuhong.Parameters Analysis of Artificial Fish Swarm Algorithm[J].Computer Engineering,2010,36(27):169-171.
[15]杨宇,潘海洋,程军圣.基于特征选择和RRVPMCD的滚动轴承故障诊断方法[J].振动工程学报,2014,27(4):629-636.
Yang Yu,Pan Haiyang,Chen Junsheng.The Rolling Bearing Fault Diagnosis Method Based on the Feature Selection and RRVPMCD[J]. Journal of Vibration Engineering,2014,27(4):629-636.