提出了一种基于改进变分模态分解（Variational Mode Decomposition，VMD）与双测度分形维数的发动机故障诊断方法。首先利用互信息法对缸盖振动信号进行端点延拓，并利用VMD算法将延拓后信号分解为多个固有模态分量（Intrinsic Mode Function，IMF），从而抑制VMD的端点效应，提高信号分解精度。然后利用正交变换方法将各IMF分量正交化，给定时间尺度序列τ=（τ₁，τ₂，…τ_n），并自适应地选择分界点将τ划分为第Ⅰ、Ⅱ尺度区间，利用各正交化的IMF分量在两个尺度区间内分别计算信号的分形维数，得到双测度分形维数，分别描述信号中的细节信息和趋势信息。最后将双测度分形维数作为特征参数输入极限学习机分类模型实现发动机故障诊断。仿真与试验结果表明：所提方法能够有效抑制VMD的端点效应，提高信号分解精度，双测度分形维数具有良好的类内聚集性和类间离散性，提高了发动机故障诊断精度。

An engine fault diagnosis method based on improved variational mode decomposition (VMD) and dual measure fractal dimension was proposed. The mutual information method was first used to extend the end of cylinder head vibration signal, VMD algorithm was then used to decompose the extended signal into several intrinsic mode functions (IMFs), and the purpose of suppressing the end effect of VMD and improving signal decomposition precision were realized. The orthogonal transform method was further used to orthogonalize each IMF component. The given time scale sequence   was divided into the first and second scale intervals according to the cut point determined by adaptive selection. The fractal dimension of signal was calculated with the orthogonalized IMF components separately in the two scale intervals and the dual measure fractal dimension was hence obtained that describes the detail information and trend information respectively. Finally, the dual measure fractal dimension was used as the input for the classification model of extreme learning machine to realize engine fault diagnosis. The simulation and experimental results show that the proposed method can effectively suppress the end effect of VMD and improve the signal decomposition accuracy. The dual measure fractal dimension has good intraclass aggregation and interclass dispersion, which improves the accuracy of engine fault diagnosis.