Fault diagnosis for rolling element bearings based on information exergy distance of acoustic emission signal
-
摘要: 在信息熵理论基础上,提出了一种融合小波能谱与马氏距离的信息距滚动轴承故障诊断方法.利用双转子试验台对滚动轴承内圈故障、外圈故障、滚动体故障、内圈滚动体故障和内圈外圈故障进行模拟,并采集其声发射信号.利用提出的信息距方法对获取的声发射信号进行分析,成功实现滚动轴承单一故障和耦合故障诊断.结果表明该方法信息利用率高于信息熵方法,能够清晰和准确地诊断出滚动轴承早期故障,效果明显优于信息熵距的诊断方法.Abstract: Based on the information entropy theory, a fault diagnosis methodology for rolling element bearings was proposed. It is a fusion of wavelet energy spectrum exergy and Mahalanobis distance. Inner ring fault, outer ring fault, rolling element fault, inner ringrolling element fault and inner ringouter ring fault of rolling element bearing were simulated on a twin spool rotor test rig. The acoustic emission signals of each fault were collected. Single fault and coupling faults of rolling element bearings were successfully diagnosed using the information exergy distance method for acoustic emission signals. The diagnosis results show that the method has higher information utilization ratio than the information entropy method. It can diagnose early faults in rolling element bearing clearly and accurately, which is proved more effective than the information entropy distance method.
-
[1] 余永增,韩龙,戴光.基于声发射的滚动轴承故障诊断方法[J].无损检测,2010,32(6):416-419. YU Yongzeng,HAN Long,DAI Guang.Fault diagnosis method for rolling bearings based on acoustic emission inspection[J].Nondestructive Testing,2010,32(6):416-419.(in Chinese) [2] 廖明夫,马振国,刘永泉,等.航空发动机中介轴承的故障特征与诊断方法[J].航空动力学报,2013,28(12):2752-2758. LIAO Mingfu,MA Zhenguo,LIU Yongquan,et al.The fault characteristics and diagnosis method of intershaft bearing in aeroengine[J].Journal of Aerospace Power,2013,28(12):2752-2758.(in Chinese) [3] Pandya D H,Upadhyay S H,Harsha S P.Fault diagnosis of rolling element bearing with intrinsic mode function of acoustic emission data using APFKNN[J].Expert Systems with Applications,2013,40(10):4137-4145. [4] Van Hecke B,Yoon J,He D.Low speed bearing fault diagnosis using acoustic emission sensors[J].Applied Acoustics,2016,105:35-44. [5] 郝如江,褚福磊,张新明.基于小波变换的滚动轴承故障声发射信号提纯技术[J].振动与冲击,2006,25(增刊):335-337. HAO Rujiang,CHU Fulei,ZHANG Xinming.Purification of accoustic emission in rolling elements defaultsbased on wavelet transform[J].Journal of Vibration and Shock,2006,25(Suppl.):335-337.(in Chinese) [6] 陈非,黄树红,杨涛,等.旋转机械振动故障的信息诊断方法[J].机械工程学报,2009,45(11):65-71. CHEN Fei,HUANG Shuhong,YANG Tao,et al.Information exergy diagnosis method of vibration faults of rotating machinery[J].Journal of Mechanical Engineering,2009,45(11):65-71.(in Chinese) [7] 徐瑞利.基于信息熵的滚动轴承声发射信号故障诊断[D].兰州: 兰州理工大学,2012. XU Ruili.Fault diagnosis of rolling acoustic emission signal based on information entropy[D].Lanzhou: Lanzhou University of Technology,2012.(in Chinese) [8] 艾延廷,费成巍.转子振动故障的小波能谱熵SVM诊断方法[J].航空动力学报,2011,26(8):1830-1835. AI Yanting,FEI Chengwei.Rotor vibration fault diagnosis methodbased on wavelet energy spectrum entropy and SVM[J].Journal of Aerospace Power,2011,26(8):1830-1835.(in Chinese) [9] 艾延廷,陈潮龙,田晶,等.基于信息熵距和 FSVM 隶属度的转子振动状态评估方法[J].推进技术,2013,34(11):1543-1548. AI Yanting,CHEN Chaolong,TIAN Jing,et al.Studies on assessing method of rotor vibration state based on information entropy distance and FSVM membership[J].Journal of Propulsion Technology,2013,34(11):1543-1548.(in Chinese) [10] 向丹,岑健.基于EMD熵特征融合的滚动轴承故障诊断方法[J].航空动力学报,2015,30(5):1149-1155. XIANG Dan,CEN Jian.Method of roller bearing fault diagnosis based on feature fusion of EMD entropy[J].Journal of Aerospace Power,2015,30(5):1149-1155.(in Chinese) [11] 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.The extracting information entropy features for rotating machinery vibration signals[J].Journal of Mechanical Engineering,2001,37(6):94-98.(in Chinese) [12] 陈非,黄来,韩彦广,等.基于频域时空特征谱的信息火用故障诊断方法[J].振动、测试与诊断,2014,34(5):898-904. CHEN Fei,HUANG Lai,HAN Yanguang,et al.Fault diagnosis methold of information exergy based on spacetiame feature spectrum in frequency domain[J].Journal of Vibration,Measurement and Diagnosis,2014,34(5):898-904.(in Chinese) [13] Paliwal D,Choudhury A,Tingarikar G.Wavelet and scalar indicator based fault assessment approach for rolling element bearings[J].Procedia Materials Science,2014,5:2347-2355. [14] 艾延廷,付琪,田晶,等.基于融合信息熵距的转子裂纹碰摩耦合故障诊断方法[J].航空动力学报,2013,28(10):2161-2166. AI Yanting,FU Qi,TIAN Jing,et al.Diagnosis method for crackrubbing coupld fault in rotor system based on integration of information entropy distance[J].Journal of Aerospace Power,2013,28(10):2161-2166.(in Chinese) [15] Rai A,Upadhyay S H.A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings[J].Tribology International,2016,96:289-306. [16] 艾延廷,陈潮龙,田晶,等.基于融合信息的转子振动故障SVM诊断方法[J].航空动力学报,2014,29(10):2464-2470. AI Yanting,CHEN Chaolong,TIAN Jing,et al.The SVM diagnosis method of rotor vibration faultsbased on integration of information exergy[J].Journal of Aerospace Power,2014,29(10):2464-2470.(in Chinese) [17] 艾延廷,冯研研,周海仑.小波变换和EEMD马氏距离的轴承故障诊断[J].噪声与振动控制,2015,35(1):235-239. AI Yanting,FENG Yanyan,ZHOU Hailun.Fault diagnosis of roller bearings using wavelet transform and EEMDMahalanobis distance[J].Noise and Vibration Control,2015,35(1):235-239.(in Chinese) [18] Tandon N,Choudhury A.A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings[J].Tribology International,1999,32(8):469-480.
点击查看大图
计量
- 文章访问数: 872
- HTML浏览量: 0
- PDF量: 441
- 被引次数: 0