General Shannon entropy spectrum of swash-platebearing based on MED and LMD
-
摘要: 针对轴承信号微弱故障特征易被强背景噪声淹没的问题,提出采用最小熵反褶积,通过逆滤波器最优化设计,对目标信号进行降噪处理,其峭度值提高了约3.8倍,增强了信号的微弱故障特征;针对非平稳非线性信号频率成分复杂难以解调的问题,提出采用局部均值分解(LMD)和峭度-相关系数筛选准则,其可对非平稳非线性信号进行自适应分解和最优重构,提高了信号的信噪比;针对信号耦合调制及边频突出的问题,通过引入广义Shannon熵进行包络谱带内降噪处理,信号一阶故障特征调制频率与故障特征频率的幅度比降低了24%~43%。通过实验室信号及某型直升机自动倾斜器轴承故障诊断地面试验的分析结果验证了该方法的合理性和可行性。
-
关键词:
- 自动倾斜器轴承 /
- 故障诊断 /
- 最小熵反褶积(MED) /
- 局部均值分解(LMD) /
- 广义Shannon熵谱
Abstract: To overcome the difficulty of swash-plate bearing early fault features of the helicopter easily immerged in strong background noise, a method based on the minimum entropy deconvolution was proposed through the optimization design of the inverse filter, its kurtosis value was increased by about 3.8 times, the vibration signal was denoised and the weak fault characteristics of vibration signal were enhanced. By using local mean decomposition (LMD) and kurtosis-correlation coefficient criterion to settle the issue of non-stationary and nonlinear signal frequency components of complex demodulation, it can adaptively decompose and optimally reconstruct the non-stationary and nonlinear signal, and improve the signal-to-noise ratio of vibration signal. To settle the issue of coupling modulation and sideband highlighting, through noise reduction in envelope spectrum by introducing the general Shannon entropy, the amplitude ratio of the first-order fault characteristic modulation frequency to the fault characteristic frequency was reduced by about 24%-43%. The diagnosis research results of laboratory signal and the fault diagnosis test system for a type of helicopter swash plate bearing verify that the method is reliable and applicable. -
[1] 张呈林,张晓谷,郭士龙,等.直升机部件设计[M].南京:教材专业编审组出版,1986. [2] 孙灿飞,王友仁.直升机行星传动轮系故障诊断研究进展[J].航空学报,2017,38(7):020892.1-020892.14.SUN Canfei,WANG Youren.Advance in the study on fault diagnosis of helicopter planetary gears[J].Acta Aeronauticaet et Astronautica Sinica,2017,38(7):020892.1-020892.14.(in Chinese) [3] 金小强,李新民,陈焕,等.基于神经网络的直升机自动倾斜器轴承故障诊断方法[J].南京航空航天大学学报,2016,48(2):230-237.JIN Xiaoqiang,LI Xinmin,CHEN Huan,et al.Investigations of helicopter swash-plate bearing fault bearing diagnosis based on neural networks[J].Journal of Nanjing University of Aeronautics and Astronautics,2016,48(2):230-237.(in Chinese) [4] 张先辉,李新民,金小强,等.基于神经网络的直升机自动倾斜器轴承故障诊断方法[J].新技术新工艺,2017,354(6):67-72.ZHANG Xianhui,LI Xinmin,JIN Xiaoqiang,et al.Fault diagnosis method of swash-plate bearing based on local Hilbert marginal spectrum[J].New Technology & New Process,2017,354(6):67-72.(in Chinese) [5] 项巍巍,蔡改改,樊薇,等.基于双调Q小波变换的瞬态成分提取及轴承故障诊断应用研究[J].振动与冲击,2015,34(10):34-39.XIANG Weiwei,CAI Gaigai,FAN Wei,et al.Transient feature extraction based on double-TQWT and its application in bearing fault diagnosis[J].Journal of Vibration and Shock,2015,34(10):34-39.(in Chinese) [6] 秦波,孙国栋,张利强,等.基于Hilbert包络谱奇异值和IPSO -SVM的滚动轴承故障诊断研究[J].机械传动,2017,41(3):166-171.QIN Bo,SUN Guodong,ZHANG Liqiang,et al.Study on the rolling bearing fault diagnosis based on the Hilbert envelope spectrum singular value and IPSO -SVM[J].Journal of Mechanical Transmission,2017,41(3):166-171.(in Chinese) [7] 武哲,杨绍普,张建超.基于LMD自适应多尺度形态学和Teager能量算子方法在轴承故障诊断中的应用[J].振动与冲击,2016,35(3):7-13.WU Zhe,YANG Shaopu,ZHANG Jianchao.Bearing fault feature extraction method based on LMD adaptive multiscale morphology and energy operator demodulating[J].Journal of Vibration and Shock,2016,35(3):7-13.(in Chinese) [8] 程军圣,于德介,杨宇.基于EMD和SVM的滚动轴承故障诊断方法[J].航空动力学报,2006,21(3):575-580.CHENG Junsheng,YU Dejie,YANG Yu.Fault diagnosis of roller bearings based on EMD and SVM[J].Journal of Aerospace Power,2006,21(3):575-580.(in Chinese) [9] 隋文涛,张丹,WANG W.基于EMD和MKD的滚动轴承故障诊断方法[J].振动与冲击,2015,34(9):55-59.SUI Wentao,ZHANG Dan,WANG W.Fault diagnosis of rolling element bearings based on EMD and MKD[J].Journal of Vibration and Shock,2015,34(9):55-59.(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] 童水光,唐宁,从飞云,等.基于奇异值分解拓展应用的故障特征提取技术[J].振动、测试与诊断,2017,37(1):65-69.TONG Shuiguang,TANG Ning,CONG Feiyun,et al.Rolling bearing fault feature extraction research based on application development of singular value decomposition[J].Journal of Vibration,Measurement and Diagnosis,2017,37(1):65-69.(in Chinese) [12] 杜冬梅,张昭,李红,等.基于LMD和增强包络谱的滚动轴承故障分析[J].振动、测试与诊断,2017,37(1):92-96.DU Dongmei,ZHANG Zhao,LI Hong,et al.Fault diagnosis for roller bearing based on local mean decomposition and enhanced envelope spectrum[J].Journal of Vibration,Measurement and Diagnosis,2017,37(1):92-96.(in Chinese) [13] 杨宇,王欢欢,程军圣,等.基于LMD的包络谱特征值在滚动轴承故障诊断中的应用[J].航空动力学报,2012,27(5):1153-1158.
YANG Yu,WANG Huanhuan,CHENG Junsheng,et al.Application of envelope spectrum characteristics based on LMD to roller bearing fault diagnosis[J].Journal of Aerospace Power,2012,27(5):1153-1158.(in Chinese)[14] 佘博,田福庆,梁伟阁.基于MED和EEMD的滚动轴承故障诊断方法[J].海军工程大学学报,2017,29(1):107-112.SHE Bo,TIAN Fuqing,LIANG Weige.Fault diagnosis of rolling element bearing based on MED and EEMD[J].Journal of Naval University of Engineering,2017,29(1):107-112.(in Chinese) [15] WALDEN A T.Non-Gaussian reflectivity,entropy and deconvolution[J].Geophys,1985,50(12):2862-2888. [16] 唐贵基,王晓龙,邓飞跃.改进增强峭度谱和增强包络谱在滚动轴承故障诊断上的应用[J].振动与冲击,2014,33(13):53-58.TANG Guiji,WANG Xiaolong,DENG Feiyue.Application of improved enhanced kurtogram and enhanced envelope spectrum in fault diagnosis of rolling bearings[J].Journal of Vibration and Shock,2014,33(13):53-58.(in Chinese) [17] 孙伟,熊邦书,黄建萍,等.小波包降噪与LMD相结合的滚动轴承故障诊断方法[J].振动与冲击,2012,31(18):153-156.SUN Wei,XIONG Bangshu,HUANG Jianping,et al.Fault diagnosis of the roller bearing using wavelet packet de-nosing and LMD[J].Journal of Vibration and Shock,2012,31(18):153-156.(in Chinese) [18] 唐贵基,王晓龙.基于EEMD降噪和1.5维能量谱的滚动轴承故障诊断研究[J].振动与冲击,2014,33(1):6-10.TANG Guiji,WANG Xiaolong.Fault diagnosis for roller bearing based on EEMD denoising and 1.5-demensional energy spectrum[J].Journal of Vibration and Shock,2014,33(1):6-10.(in Chinese)
点击查看大图
计量
- 文章访问数: 906
- HTML浏览量: 1
- PDF量: 1038
- 被引次数: 0