基于KVMD-PWVD与LNMF的内燃机振动谱图像识别诊断方法

牟伟杰,石林锁,蔡艳平,孙钢,郑勇

振动与冲击 ›› 2017, Vol. 36 ›› Issue (2) : 45-51.

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振动与冲击 ›› 2017, Vol. 36 ›› Issue (2) : 45-51.
论文

基于KVMD-PWVD与LNMF的内燃机振动谱图像识别诊断方法

  • 牟伟杰,石林锁,蔡艳平,孙钢,郑勇
作者信息 +

IC engine fault diagnosis method based on KVMD-PWVD and LNMF

  • MU Weijie,SHI Linsuo,CAI Yanping,SUN Gang,ZHENG Yong
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摘要

为了直接对内燃机振动谱图像进行诊断识别,提出一种基于改进变分模态分解(VMD)、伪魏格纳时频分析(PWVD)与局部非负矩阵分解(LNMF)的内燃机振动谱图像识别诊断方法。该方法首先针对VMD分解过程中的层数选取问题,提出了一种中心频率筛选的VMD分解层数改进方法(KVMD),然后将内燃机振动信号利用KVMD分解成一组单分量模态信号,并对生成的各个单分量信号进行伪魏格纳分析处理后表征成振动谱图像;在此基础上,对生成的内燃机KVMD-PWVD振动谱图像分别采用非负矩阵分解(NMF)和LNMF形成编码矩阵,并采用最近邻分类器、朴素贝叶斯分类器和支持向量机对上述编码矩阵直接进行模式识别,以实现内燃机振动谱图像的自动诊断。最后,将该方法应用在内燃机故障诊断实例中,结果表明:该方法改进了传统图像模式识别中的特征参数方法,能有效诊断出内燃机气门间隙故障,三种分类器识别精度均大于93%,其中支持向量机的分类精度最高,达到99.8%,且采用LNMF形成的编码矩阵识别精度整体高于NMF,为内燃机振动诊断探索了一条新途径。

Abstract

In order to directly diagnose and recognize IC engine vibration spectrum images,based on the improved variational mode decomposition (VMD) ,pseudo Wigner-Ville time-frequency analysis (PWVD) and local non-negative matrix factorization (LNMF),an IC engine vibration spectrum image recognition and diagnosis method was proposed. Aiming at the VMD layers selection during the decomposition process,a center frequency selected VMD decomposition method (KVMD) was proposed,then the vibration signal of IC engine was decomposed into a set of single component modal signals by KVMD,and each single component of the signal,by using PWVD,was characterized as a vibration spectral image. On this basis,to get a code matrix,the non-negative matrix factorization (NMF) and local non-negative matrix factorization (LNMF) were used to the IC engine KVMD-PWVD vibration spectral image,and the KNNC,NBC and SVM were applied to the code matrix for pattern recognition in order to realize the automatic diagnosis of vibration spectrum images. The method has been used in practical IC engine fault diagnosis and the results show that the method improves the traditional characteristic parameters of image pattern recognition,it can effectively diagnose the IC engine valve clearance fault,the recognition accuracy of the three classifiers is all not less than 93%,the SVM has the highest classification accuracy which reaches 99.8%,and the code matrix using the LNMF has higher accuracy than the NMF. The method explores a new way for the IC engine vibration diagnosis.

关键词

内燃机 / 故障诊断 / 时频分布 / 特征提取 / 局部非负矩阵

Key words

internal combustion (IC) engine / fault diagnosis / time-frequency distribution / feature extraction / local non-negative matrix factorization(LNMF)

引用本文

导出引用
牟伟杰,石林锁,蔡艳平,孙钢,郑勇 . 基于KVMD-PWVD与LNMF的内燃机振动谱图像识别诊断方法[J]. 振动与冲击, 2017, 36(2): 45-51
MU Weijie,SHI Linsuo,CAI Yanping,SUN Gang,ZHENG Yong. IC engine fault diagnosis method based on KVMD-PWVD and LNMF[J]. Journal of Vibration and Shock, 2017, 36(2): 45-51

参考文献

[1] 蔡艳平,李艾华,王涛,等.基于时频谱图与图像分割的柴油机故障诊断[J] .内燃机学报,2011,29(2):181-186.
CAI Yan-ping,LI Ai-hua,WANG Tao,et al.I.C. Diesel Engine Fault Diagnosis Based on Time-Frequency  Spectrum Image and Image Segmentation [J]. Transactions of CSICE,  2011,29(2):181-186.
[2] 李兵,徐榕,贾春宁,等.基于自适应形态提升小波与改进非负矩阵分解的发动机故障诊断方法[J].兵工学报,2013,34(3):353-360.
LI Bing, XU Rong, JIA Chun-ning, et.al.Engine fault diagnosis Utilizing adaptive morphological lifting wavelet and improved non-negative matrix factorization[J].Acta Armamentarii, 2013,34(3):353-360.
[3] 任金城,张玲玲,肖云魁,等.基于对称极坐标和图像处理的柴油机故障诊断研究[J].车用发动机,2013, 6(209):80-85.
REN Jin-cheng, ZHANG Ling-ling, XIAO Yun-kui, et.al. Diesel Engine Fault Diagnosis Based on Symmetrical Polar Coordinate and Image Processing [J].VEHICLE ENGINE, 2013,6(209):80-85.
[4] 沈虹,赵红东,梅检民,等.基于高阶累积量图像特征的柴油机故障诊断研究[J].振动与冲击,2015,34(11):133-138.
SHEN Hong, ZHAO Hong-dong, MEI Jian-min, et al. Diesel engine fault diagnosis based on high-order cumulant image features[J]. Journal of Vibration and Shock, 2015,34(11):133-138.
[5] 毛永芳,秦树.重分配谱图和多窗谱在机械故障诊断中的应用[J].振动与冲击,2009,28(1):161-165.
      Mao Y F,Qin S. Re-allocation of spectrum and multi-spectral windows and its application in machinery fault diagnosis [J].Journal of Vibration and Shock,2009,28(1):161-165.
[6] 王成栋,张优云,夏 勇. 基S变换的柴油机气阀机构故障诊断研究[J].内燃机学报,2003,21(4):271-275.
WANG Cheng-dong,ZHANG You-yun,XIA Yong.Fault diagnosis for diesel valve train based on S Transform[J]. Transaction of CSICE, 2003,21(4):271-275.
[7] 王成栋,张优云,夏 勇. 模糊函数图像在柴油机气阀故障诊断中的应用研究[J]. 内燃机学报,2004,22(4):162-168.
WANG Cheng-dong,ZHANG You-yun,XIA Yong.Study on the use of ambiguity function images in fault diagnosis for diesel valve train[J]. Transaction of CSICE, 2004,22(4):162-168.
[8] Huang N E, Shen Z, et al. The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis[C] // Proceedings of the Royal Society of London, Series A. 1998,454:903-995.
[9] Dragomiretskiy,K.,et al. Variational Mode Decomposition [J], IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 62, NO. 3, FEBRUARY 1, 2014,pp.531-544.
[10]Li S Z, Hou X W, Zhang H J, et al. Learning spatially localized,parts-based representation[J]. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2001,1:207-212.
[11] 唐贵基,王晓龙. 参数优化变分模态分解方法在滚动轴承早期故障诊断中的应用[J].西安交通大学学报,2015,49(5):73-80.
TANG Guiji, WANG Xiaolong. Parameter Optimized Variational Mode Decomposition Method with Application to Incipient Fault Diagnosis of Rolling Bearing[J].Journal of Xi’an JiaoTong University, 2015,49(5):73-80.
[12]蔡艳平,李艾华,王涛,等.基于EMD-Wigner-Ville的内燃机振动时频分析[J].振动工程学报,2010,23(4):430-437.
CAI Yan-ping,LI Ai-hua,WANG Tao,et al.I.C. engine vibration time-frequency analysis based on EMD-Wigner-Ville[J].Journal of Vibration Engineering,  2010,23(4):430-437.
[13] Lee D D, Seung H S. Learning the parts of objects by no n-negative matrix factorization [J].Nature,1999,401(6755):788-791.
[14]刘昱昊.基于非负矩阵分解算法的人脸识别技术的研究[D]. 吉林:吉林大学,2014.
LIU Yuhao. Research on NMF-based Algorithms applying to face recognition [D].Ji Lin: Ji Lin University,2014.
[15]袁宝华, 王欢,任明武. LBP与LNMF特征融合的人脸识别[J].计算机工程与应用, 2013, 49(5):166-169.
YUAN Baohua,WANG Huan,Ren Mingwu.Fusing local binary pattern and LNMF of face recognition[J].Computer Engineering and Application, 2013, 49(5):166-169.
[16]向阳辉,张干清,庞佑霞.结合SVM和改进证据理论的多信息融合故障诊断[J].振动与冲击,2015,34(13):71-77.
XIANG Yanghui, ZHANG Ganqing, PANG Youxia. Multi-information fusion fault diagnosis using SVM & improved evidence theory[J]. Journal of Vibration and Shock, 2015,34(13):71-77.
 

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