基于IVMD与改进KELM的发动机故障诊断
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TH137;TK41.1

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国家自然科学基金资助项目(51305454)


Engine Fault Diagnosis Based on Independent Variational Mode Decomposition and Improved Kernel Extreme Learning Machine
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    摘要:

    为从含有较强噪声的缸盖振动信号中提取有效的故障特征并进行故障分类,提出了采用独立变分模态分解(independent variational mode decomposition,简称IVMD)与改进核极限学习机(improved kernel extreme learning machine,简称IKELM)的发动机故障诊断方法。首先,根据频谱循环相干系数选取匹配波形对信号进行端点延拓,并利用变分模态分解(variational mode decomposition,简称VMD)将延拓后信号分解为一系列固有模态分量,有效抑制了VMD中的端点效应;其次,选取有效分量作为输入观测信号,进行核独立成分分析,进一步分离干扰噪声与有效信号,并消除模态混叠,得到相互独立的有效故障特征频带,进而提取各频带的自回归模型参数、多尺度模糊熵和标准化能量矩构建故障特征向量集;最后,建立基于社会情感优化算法的IKELM分类模型,对故障特征进行分类,实现发动机故障诊断。仿真和实验结果表明,所提出的方法可有效抑制VMD的端点效应,提高信号分解精度,消除噪声干扰并分离出相互独立的有效故障特征频带,增强特征参数辨识度,最终提高发动机故障诊断速度与精度,发动机故障诊断平均准确率达到99.85%。

    Abstract:

    In order to extract effective fault features from the cylinder head vibration signal under strong noise background and classify faults, an engine fault diagnosis method of independent variational mode decomposition (IVMD) combined with improved kernel extreme learning machine is proposed. Firstly, the matching waveform is selected to carry out the end extension of the original signal according to the spectral cyclic coherence coefficient. And the signal after end extension is decomposed into a number of intrinsic mode functions (IMFs) by using variational mode decomposition (VMD). The end effect in VMD is suppressed effectively. Then the selected effective IMFs and original signal are constructed as the input observation signals of kernel independent component analysis (KICA). After KICA, the noise and effective signal are separated further, the mode mixing is eliminated and the independent effective fault feature bands are obtained. Autoregressive model parameters, multi-scale fuzzy entropy and normalized energy moment of each frequency band are extracted to construct a joint fault feature vector. Lastly the improved KELM model based on social emotional optimization algorithm (SEOA-KELM) is constructed to classify the fault features in order to realize the engine fault diagnosis. The simulation and experimental results show that the proposed method can effectively suppress the end effect in VMD, improve the signal decomposition precision, eliminate the noise,separate independent and effective fault feature frequency bands, enhance feature parameter identification and improve the speed and accuracy of engine fault diagnosis finally. The average accuracy rate of engine fault diagnosis is up to 99.85%.

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  • 在线发布日期: 2019-08-26
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