转子振动故障的小波能谱熵SVM诊断方法

艾延廷; 费成巍

转子振动故障的小波能谱熵SVM诊断方法

沈阳航空航天大学动力与能源工程学院,沈阳 110136

基金项目: 航空科学基金(2008ZB54006)

计量
- 文章访问数: 1534
- HTML浏览量: 0
- PDF量: 27
- 被引次数: 0
出版历程
- 收稿日期: 2010-08-03
- 修回日期: 2011-01-20
- 刊出日期: 2011-08-28

Rotor vibration fault diagnosis method based on wavelet energy spectrum entropy and SVM

College of Power and Energy Engineering, Shenyang Aerospace University,Shenyang 110136,China

摘要

摘要: 融合小波能谱熵和支持向量机(SVM)的特点,提出了基于小波能谱熵的SVM故障诊断方法.利用转子试验台对转子典型振动故障进行模拟并采集振动数据,提取其振动信号的小波能谱熵作为特征向量,通过样本训练建立了转子在各种典型振动故障状态下的SVM模型和多类分类器,进而实现了对未知转子振动故障的识别.实际应用表明,提出的转子振动故障诊断方法是可行和有效性的.
- 转子振动 /
- 故障诊断 /
- 小波能谱熵 /
- 支持向量机 /
- 特征向量 /
- 故障模拟
Abstract: A method for rotor vibration fault diagnosis was proposed based on wavelet energy spectrum entropy and support vector machine (SVM) by fusing their advantages.The typical rotor vibration faults were simulated on the rotor test-bed and vibration signals were collected at the same time.The SVM model and multi-classifier for rotor typical faults diagnosis were established through training of the samples obtained,and the unknown rotor faults were recognized consequently.It is proved by practical applications that the method proposed in this paper for diagnosing rotor vibration faults is workable and effective.
- rotor vibration /
- fault diagnosis /
- wavelet energy spectrum entropy /
- support vector machine (SVM) /
- feature vector /
- fault simulation

HTML

参考文献(14)

[1]	费成巍,艾延廷.航空发动机健康管理系统设计技术[J].航空发动机,2009,35(5):24-29. FEI Chengwei,AI Yanting.Design technology of aircraft engine health management system[J].Aeroengine,2009,35(5):24-29.(in Chinese)
[2]	屈梁生,张西宁,沈玉娣.机械故障诊断理论与方法[M].西安:西安交通大学,2009.
[3]	Ling J,Qu L S.Feature extraction based on Morlet wavelet and its application for mechanical fault diagnosis[J].Journal of Sound and Vibration,2000,234(1):135-148.
[4]	ZHANG Hanlei,ZHOU Jiemin,LI Gang.Improved wavelet analysis for induction motors mixed-fault diagnosis[J].Frontiers of Electrical and Electronic Engineering in China,2007,2(3):355-360.
[5]	曾庆虎,刘冠军,邱静.基于小波相关特征尺度熵的预测特征信息提取方法研究[J].中国机械工程,2008,19(10):1193-1196. ZENG Qinghu,LIU Guanjun,QIU Jing.Research on approach of prognostic feature information extraction based on wavelet correlation feature scale entropy[J].Chinese Machine Engineering,2008,19(10):1193-1196.(in Chinese)
[6]	Volponi A J,Brotherton T,Luppold R,et al.Development of an information fusion system for engine diagnostics and health management.AIAA 2004-6461,2004.
[7]	白鹏,张喜斌,张斌,等.支持向量机理论及工程应用实例[M].西安:西安电子科技大学出版社,2008.
[8]	张亚楠,魏武,武林林.基于小波Shannon熵SVM和遗传算法的电机机械故障诊断[J].电力自动化设备,2010,30(1):87-91. ZHANG Yanan,WEI Wu,WU Linlin.Motor mechanical fault diagnosis based on wavelet packet shannon entropy SVM and GA[J].Electric Power Automation Equipment,2010,30(1):87-91.(in Chinese)
[9]	LI Juntao,JIA Yingmin.Huberized multiclass support vector machine for microarray classification[J].Acta Automatica Sinica,2010,36(3):399-405.
[10]	印欣运,何永勇,彭志科,等.小波熵及其在状态趋势分析中的应用[J].振动工程学报,2004,17(2):165-169. YIN Xinyun,HE Yongyong,PENG Zhike,et al.Study on wavelet entropy and its applications in trend analysis[J].Journal of Vibration Engineering,2004,17(2):165-169.(in Chinese)
[11]	陈非,黄树红,张燕平,等.基于过程的旋转机械振动故障定量诊断方法[J].动力工程,2008,28(4):543-547. CHEN Fei,HUANG Shuhong,ZHANG Yanping,et al.A quantitative diagnosis method of vibration faults of rotating machinery based on process[J].Chinese Journal of Power Engineering,2008,28(4):543-547.(in Chinese)
[12]	申弢,黄树红,韩守木,等.旋转机械振动信号的信息熵特征[J].机械工程学报,2001,37(6):94-98. SHEN Tao,HUANG Shuhong,HAN Shoumu,et al.Extracting information entropy features for rotating machinery vibration signals[J].Chinese Journal of Mechanical Engineering,2001,37(6):94-98.(in Chinese)
[13]	谭晓栋,邱静,刘冠军,等.基于小波能谱熵-隐半马尔可夫模型的故障识别方法应用[J].机械科学与技术,2009,28(10):1341-1348. TAN Xiaodong,QIU Jing,LIU Guanjun,et al.A fault state recognition method based on wavelet energy entropy and hidden semi-Markov models and its applications[J].Mechanical Science and Technology for Aerospace Engineering,2009,28(10):1341-1348.(in Chinese)
[14]	LI Juntao,JIA Yingmin.Huberized multiclass support vector machine for microarray classification[J].Acta Automatica Sinica,2010,36(3):399-405.