柴油机振动信号自适应分解方法的比较*

doi:10.19562/j.chinasae.qcgc.2018.010.008

摘要/Abstract

摘要： 变分模态分解是一种新的自适应分解方法,为检验其对柴油机信号的适用性,建立多分量、调幅调频和含噪仿真信号,采用变分模态分解法对其进行分解,并与其它自适应分解方法从分解效果、抑制模态混叠和端点效应能力等方面进行比较;接着分解柴油机瞬变工况的振动信号,发掘曲轴轴承磨损信号变化规律,提取故障特征;最后利用支持向量机进行故障类型识别,进一步验证该方法的有效性。结果表明:变分模态分解在分解效果、抑制模态混叠和端点效应能力等方面均优于其他自适应分解方法,适用于柴油机状态监测和故障诊断。

关键词: 柴油机, 振动信号, 自适应分解, 变分模态分解, 故障诊断

Abstract: Variational mode decomposition (VMD) is a new adaptive decomposition method. In order to check its suitability for diesel engine signals, a multi-component, AM-FM simulation signal is built with white noise added, which is then decomposed by using VMD and compared with other adaptive decomposition methods in terms of decomposition effects and the capability to suppress modal aliasing and endpoint effects. Then, the vibration signals of diesel engine under transient conditions are decomposed, the changing pattern of wear signals of crankshaft bearing is explored and fault features are extracted. Finally fault types are identified by using support vector machine, to further verify the effectiveness of the method adopted. The results show that VMD method is better than other adaptive decomposition methods in decomposition results and the capability to suppress modal aliasing and endpoint effects, suitable for the state monitoring and fault diagnosis of diesel engine

Key words: diesel engine, vibration signal, adaptive decomposition, VMD, fault diagnosis

贾继德, 任刚, 贾翔宇, 韩佳佳. 柴油机振动信号自适应分解方法的比较^*[J]. 汽车工程, 2018, 40(10): 1172-1178.

Jia Jide, Ren Gang, Jia Xiangyu, Han Jiajia. Comparison of Adaptive Decomposition Methods for Diesel Engine Vibration Signals[J]. , 2018, 40(10): 1172-1178.

参考文献

[1] 贾继德,张玲玲,曾锐利,等.变速箱齿轮磨损故障的极坐标角频表示与诊断[J].农业工程学报,2012,28(22):58-62.
[2] 周士帅,窦东阳,薛斌.基于LMD和MED的滚动轴承故障特征提取方法[J].农业工程学报,2016,32(23):70-76.
[3] 贾继德.汽车故障诊断中的信号处理方法[M].北京:化学工业出版社,2015.
[4] 程军圣,杨怡,杨宇.局部特征尺度分解方法及其在齿轮故障诊断中的应用[J].机械工程学报,2012,48(9):64-71.
[5] HUANG N E, SHEN Zheng, LONG S R. A new view of nonlinear water waves: the Hilbert spectrum[J]. Annu. Rev. Fluid Mech.,1999,31:417-457.
[6] RAI V K, MOHANTY A R. Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform[J]. Mechanical Systems and Signal Processing,2007,21(6):2607-2615.
[7] RAI V K, MOHANTY A R. Bearing fault diagnosis fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform[J]. Mechanical Systems and Signal Processing,2007,21(6):2607-2615.
[8] 周义,李鸿光.快速自适应经验模态分解方法的基本原理及其性能评估[J].振动与冲击,2016,35(3):14-19.
[9] WU Z H, HUANG N E. Ensemble empirical mode decomposition: a noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis,2009,1(1):1-41.
[10] 吴小涛,杨锰,袁晓辉,等.基于峭度准则EEMD及改进形态滤波方法的轴承故障诊断[J].振动与冲击,2015,34(2):38-44.
[11] TORRES M E, COLOMINAS M A, SCHLOTTHAUER G, et al. A complete ensemble empirical mode decomposition with adaptive noise[C]. Acoustics: 2011 IEEE International Conference on Speech and Signal Processing (ICASSP),2011:4144-4147.
[12] SMITH J S. The local mean decomposition and its application to EEG percept ion data[J]. Journal of the Royal Society Interface,2005,2(5):443-454.
[13] 程军圣,郑近德,杨宇.一种新的非平稳信号分析方法—局部特征尺度分解法[J].振动工程学报,2012,25(2):215-220.
[14] FREI M G, OSORIO I. Intrinsic time-scale decomposition: time-frequency-energy analysis and real-time filtering of non-stationary signals[J]. Proc. Royal Soc. A,2007,463:321-342.
[15] DRAGOMIRETSKIY K, ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signal Proce-ssing,2013,62(3):531-544.
[16] 唐贵基,王晓龙.变分模态分解方法在滚动轴承早期故障诊断中的应用[J].振动工程学报,2016,29(4):638-648.
[17] ZHAO C, FENG Z P. Application of multi-domain sparse features for fault identification of planetary gearbox[J]. Measurement,2017,104:169-179.
[18] AN X L, TANG Y J. Application of variational mode decomposition energy distribution to bearing fault diagnosis in a wind turbine[J]. Transactions of the Institute of Measurement and Control,2017,39(7):1000-1006.
[19] AN X L, TANG Y J. Denoising of hydropower unit vibration signal based on variational mode decomposition and approximate entropy[J]. Transactions of the Institute of Measurement and Control,2016,38(3):282-292.
[20] LI Z P, CHEN J L, ZI Y Y. Independence-oriented VMD to identify fault feature for wheel set bearing fault diagnosis of high speed locomotive[J]. Mechanical Systems and Signal Processing,2017,85:512-529.
[21] 唐贵基,王晓龙.IVMD融合奇异值差分谱的滚动轴承早期故障诊断[J].振动、测试与诊断,2016,36(4):700-707.
[22] 赵磊,朱永利,高艳丰,等.基于变分模态分解和小波分析的变压器局部放电去噪研究[J].电测与仪表,2016,53(11):13-18.
[23] 贾继德,吴春志,贾翔宇,等.一种适用于发动机振动信号的时频分析方法[J].汽车工程,2017,39(1):97-101.
[24] 马增强,李亚超,刘政,等.基于变分模态分解和Teager能量算子的滚动轴承故障特征提取[J].振动与冲击,2016,35(13):134-139.

柴油机振动信号自适应分解方法的比较^*

Comparison of Adaptive Decomposition Methods for Diesel Engine Vibration Signals

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