基于粗糙集属性约简和贝叶斯分类器的故障诊断

中国机械工程 ›› 2015, Vol. 26 ›› Issue (14): 1969-1977.

基于粗糙集属性约简和贝叶斯分类器的故障诊断

姚成玉1;李男1;冯中魁1;陈东宁2,3

1.燕山大学河北省工业计算机控制工程重点实验室,秦皇岛,066004
2.燕山大学河北省重型机械流体动力传输与控制重点实验室,秦皇岛,066004
3.燕山大学先进锻压成形技术与科学教育部重点实验室,秦皇岛,066004

出版日期:2015-07-25 发布日期:2015-08-05
基金资助:
国家自然科学基金资助项目(51405426)；河北省教育厅科研项目(ZH2012062）

Fault Diagnosis Based on Rough Set Attribute Reduction and Bayesian Classifier

Yao Chengyu1;Li Nan1;Feng Zhongkui1;Chen Dongning2，3

1.Key Laboratory of Industrial Computer Control Engineering of Hebei Province,Yanshan University, Qinhuangdao, Hebei, 066004
2.Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao, Hebei, 066004
3.Key Laboratory of Advanced Forging & Stamping Technology and Science,Ministry of Education,Yanshan University, Qinhuangdao, Hebei, 066004

Online:2015-07-25 Published:2015-08-05
Supported by:
National Natural Science Foundation of China（No. 51405426）;Hebei Provincial Scientific Research Project of Ministry of Education of China（No. ZH2012062)

摘要/Abstract

摘要：

利用改进的小波包对收集的信号进行特征提取,解决了小波包分解的频率混叠问题；针对故障信息中的冗余属性问题,提出了基于类差别矩阵改进属性重要度的属性约简算法,根据各条件属性在类差别矩阵中出现1的频次定义新的属性重要度,提高属性约简的效率;通过考虑条件属性与类属性间的关联性,提出了基于熵权法的属性加权朴素贝叶斯分类器算法,提高故障分类精度。通过对滚动轴承故障数据的对比分析,验证了所提组合方法在提高故障诊断正确率、快速性方面所具有的优势。

关键词: 故障诊断, 改进小波包, 粗糙集, 属性约简, 属性加权朴素贝叶斯分类器

Abstract:

An improved wavelet package was used to extract feature of collected signals and to solve the wavelet packet aliasing problem.Considering redundant attributes in fault informations,rough set attribute reduction algorithm was proposed based on class discernibility matrix and improved attribute significance,new attribute significance was defined according to the frequency of each condition attribute equal to 1 in the class discernibility matrix,which improved the efficiency of attribute reduction. Considering the relativity among different condition attributes and class attributes,the entropy weight method-based attribute weighted naive Bayesian classifier algorithm was proposed,which improved the fault classification accuracy.By comparative analysis of rolling bearing failure data,it shows that the proposed hybrid method herein has certain advantages in fault diagnosis accuracy and rapidity.

Key words: fault , diagnosis;improved wavelet package;rough , set;attribute , reduction;attribute , weighted naive , Bayesian , classifier

中图分类号:

姚成玉, 李男, 冯中魁, 陈东宁, . 基于粗糙集属性约简和贝叶斯分类器的故障诊断[J]. 中国机械工程, 2015, 26(14): 1969-1977.

Yao Chengyu, Li Nan, Feng Zhongkui, Chen Dongning, . Fault Diagnosis Based on Rough Set Attribute Reduction and Bayesian Classifier[J]. China Mechanical Engineering, 2015, 26(14): 1969-1977.

参考文献

[1]王国彪，何正嘉，陈雪峰，等.机械故障诊断基础研究“何去何从”[J].机械工程学报，2013，49(1)：63-72.
Wang Guobiao,He Zhengjia,Chen Xuefeng,et al.Basic Research on Machinery Fault Diagnosis-What Is the Prescription[J]. Journal of Mechanical Engineering,2013,49(1):63-72.
[2]Xian Guangming,Zeng Biqing.An Intelligent Fault Diagnosis Method Based on Wavelet Packer Analysis and Hybrid Support Vector Machines[J].Expert Systems with Applications,2009,36(10):12131-12136.
[3]王冬云，张文志.基于小波包变换的滚动轴承故障诊断[J].中国机械工程，2012，23(3)：295-298.
Wang Dongyun,Zhang Wenzhi.Fault Diagnosis Study of Ball Bearing Based on Wavelet Packet Transform[J].China Mechanical Engineering,2012,23(3):295-298.
[4]钱苏翔,杨世锡,焦卫东,等.基于独立分量分析与小波包分解的混叠声源信号波形恢复[J].中国机械工程,2010,21(24):2956-2961.
Qian Suxiang,Yang Shixi,Jiao Weidong,et al.Waveform Restoral of Multiple Mixed Acoustical Source Signals Based on Independent Component Analysis and Wavelet Packet Decomposition[J].China Mechanical Engineering, 2010,21(24):2956-2961.
[5]高英杰,孔祥东,Zhang Qin.基于小波包分析的液压泵状态监测方法[J].机械工程学报,2009,45(8):80-88.
Gao Yingjie,Kong Xiangdong,Zhang Qin.Wavelet Packets Analysis Based Method for Hydraulic Pump Condition Monitoring[J]. Journal of Mechanical Engineering,2009,45(8):80-88.
[6]Morsi W G,El-Hawary M E.A New Reactive,Distortion and Non-Active Power Measurement Method for Nonstationary Waveforms Using Wavelet Packet Transform[J].Electric Power Systems Research，2009，79(10):1408-1415.
[7]张邦基，于德介，杨胜.基于小波变换与粗集理论的滚动轴承故障诊断[J].中国机械工程,2008,19(15):1793-1831.
Zhang Bangji,Yu Dejie,Yang Sheng. Roller Bearing Fault Diagnosis Approach Based on Wavelet Transform and Rough Set Theory[J].China Mechanical Engineering,2008,19(15):1793-1831.
[8]Qian J,Miao D Q,Zhang Z H,et al.Hybrid Approaches to Attribute Reduction Based on Indiscernibility and Discernibility Relation[J].International Journal of Approximate Reasoning,2011,52(2):212-230.
[9]吕萍,钱进,王波,等.一种快速差别矩阵属性约简算法[J].计算机工程与应用,2010,46(20)：164-167.
Lü Ping,Qian Jin,Wang Bo,et al.A Fast Algorithm for Attribute Reduction on Discernibility Matrix[J].Computer Engineering and Applications,2010,46(20):164-167.
[10]Dai Jianhua,Wang Wentao,Tian Haowei,et al.Attribute Selection Based on a New Conditional Entropy for Incomplete Decision Systems[J].Knowledge-Based Systems,2013,39:207-213.
[11]蒋瑜,王鹏,王燮,等.基于差别矩阵的属性约简完备算法[J].计算机工程与应用,2007,43(19):185-187.
Jiang Yu,Wang Peng,Wang Xie,et al.Complete Algorithm for Attribute Reduction Based on Discernibility Matrix[J].Computer Engineering and Applications,2007,43(19):185-187.
[12]吴定海,张培林,任国全,等.基于Bayes的超球分类器及在柴油机异常检测中的应用[J].机械工程学报,2011,47(6):22-26.
Wu Dinghai,Zhang Peilin,Ren Guoquan,et al.Bayes-based Hyper-sphere Classification and Its Application in Diesel Engine Abnormity Detection[J].Journal of Mechanical Engineering,2011,47(6):22-26.
[13]Youn E,Jeong M K.Class Dependent Feature Scaling Method Using Naive Bayes Classifier for Text Datamining[J]. Pattern Recognition Letters,2009,30(5):477-485.
[14]Mukherjee S,Sharma N.Intrusion Detection Using Naive Bayes Classifier with Feature Reduction[J].Procedia Technology,2012,4:119-128.
[15]Zhang H,Sheng S.Learning Weighted Naive Bayes with Accurate Ranking[C]//Proceedings of the Fourth IEEE International Conference on Data Mining.Brighton,2004:567-570.
[16]Case Western Reserve University Bearing Data Center. Download a Data File.http://csegroups. case. edu/bearingdatacenter/pages/download-data-file.
[17]吴强，孔凡让，何清波，等.基于小波变换和ICA的滚动轴承早期故障诊断[J].中国机械工程,2012,23(7):835-840.
Wu Qiang,Kong Fanrang,He Qingbo,et al.Early Fault Diagnosis of Rolling Element Bearings Based on Wavelet Transform and Independent Component Analysis[J].China Mechanical Engineering,2012,23(7):835-840.
[18]程军圣,马兴伟,李学军,等.基于OC-VPMCD和ITD的滚动轴承故障诊断方法[J].中国机械工程,2014,25(11):1492-1497.
Cheng Junsheng,Ma Xingwei,Li Xuejun,et al.Rolling Bearing Fault Diagnosis Method Based on OC-VPMCD and ITD[J].China Mechanical Engineering,2014,25(11):1492-1497.
[19]欧璐,于德介.基于拉普拉斯分值和模糊C均值聚类的滚动轴承故障诊断[J].中国机械工程,2014,25(10):1352-1357.
Ou Lu,Yu Dejie.Rolling Bearing Fault Diagnosis Based on Laplacian Score and Fuzzy C-Means Clustering[J].China Mechanical Engineering,2014,25(10): 1352-1357.