汽车工程

汽车工程 ›› 2019, Vol. 41 ›› Issue (12): 1442-1449.doi: 10.19562/j.chinasae.qcgc.2019.012.014

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变分模态分解的Volterra模型和形态学分形维数在发动机故障诊断中的应用*

周小龙1, 刘薇娜2, 姜振海3, 马风雷3   

  1. 1.北华大学机械工程学院,吉林 132021;
    2.长春理工大学机电工程学院,长春 130022;
    3.长春工业大学机电工程学院,长春 130012
  • 发布日期:2019-12-25
  • 通讯作者: 周小龙,讲师,博士,E-mail:z85217479@163.com
  • 基金资助:
    *国家自然科学基金(51505038)和吉林省科技厅重点科技攻关项目(KYC-JC-XM-2017-042)资助

Application of Volterra Mode of Variational Mode Decomposition and Morphology Fractal Dimension in Engine Fault Diagnosis

Zhou Xiaolong1, Liu Weina2, Jiang Zhenhai3, Ma Fenglei3   

  1. 1.Mechanical Engineering College, Beihua University, Jilin 132021;
    2.College of Mechanical and Electric Engineering, Changchun University of Science and Technology, Changchun 130022;
    3.School of Mechatronic Engineering, Changchun University of Technology, Changchun 130012
  • Published:2019-12-25

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摘要： 针对实测发动机故障信号的非线性和形态学分形维数难以对其有效估计的问题,提出一种基于变分模态分解(variational mode decomposition, VMD)的Volterra模型和形态学分形维数相结合的发动机故障诊断方法。首先采用VMD方法对发动机故障信号进行分解,通过基于互信息熵能量熵增量的虚假固有模态函数(intrinsic mode function, IMF)分量剔除算法,将噪声和虚假干扰成分从信号内分离,对含有故障信息的敏感IMF分量重构,然后通过对重构信号相空间的重构,建立Volterra自适应预测模型,获取模型参数,最后计算模型参数向量的形态学分形维数,并将其作为量化的特征参数用于发动机工作状态和故障类型的识别。通过对实测发动机声振信号的分析,结果表明,该方法可有效提取发动机的状态特征信息,实现发动机异响的故障诊断。

关键词: 发动机, 故障诊断, 变分模态分解, Volterra预测模型, 数学形态学, 分形维数

Abstract: A novel engine fault diagnosis method based on volterra mode of variational mode decomposition (VMD) and morphology fractal dimension is proposed to solve the problem of the nonlinearity of the measured engine fault signal and that morphology fractal dimension can not estimate this signal effectively. Firstly, the engine fault signal is decomposed by VMD method, and the noise and false interference components are separated from the signal by the component elimination algorithm of the false intrinsic mode function (IMF) based on mutual information entropy and energy entropy increment. The sensitive IMF components with fault information are reconstructed. Then the Volterra adaptive prediction model is established by reconstructing the phase space of reconstructed signal to obtain the model parameters. Finally, the morphology fractal dimension of the model parameter matrix is calculated and these characteristic parameters are used to identify engine working states and fault types. Through analysis of the measured engine sound vibration signals at different states, the experimental results show that the proposed method can effectively extract the state information characteristics of the engine and realize fault diagnosis for engine abnormal sound signals.

Key words: engine, fault diagnosis, variational mode decomposition, Volterra prediction model, mathematical morphology, fractal dimension