基于振动时频图像全局和局部特征融合的柴油机故障诊断

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摘要提出了一种基于振动时频图像全局和局部特征融合的柴油机故障诊断方法。该方法首先采用平滑伪维格纳分布（SPWVD）方法生成柴油机振动时频图像，然后分别用核主元分析（KPCA）和局部非负矩阵分解（LNMF）方法提取时频图像的全局和局部特征进行融合，并用独立分量（ICA）分析方法对融合后的特征进行降维，最后对降维后的融合特征进行分类完成对柴油机的故障诊断。试验结果表明，基于振动时频图像全局和局部特征融合的柴油机故障诊断方法，能够准确诊断柴油机的气门故障。

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	牟伟杰1.2
	石林锁1
	蔡艳平1
	郑勇1
	刘浩1

关键词 ：柴油机, 故障诊断, 时频图像, 全局特征, 局部特征, 特征融合

Abstract：The global and local features fusion of a timefrequency image was introduced into the diesel engine fault diagnosis. Timefrequency images of a diesel engine were generated by the method of smoothed pseudo wignerville distribution (SPWVD). Then, the kernel principal component analysis (KPCA) and local nonnegative matrix factorization (LNMF) method were used to extract its global and local features, and the independent component analysis (ICA) method was used for the dimension reduction of the characteristics after fusion. Finally, the fused features were classified to complete the diesel engine fault diagnosis.

Key words： diesel fault diagnosis time-frequency image global feature local feature feature fusion

收稿日期: 2016-11-24 出版日期: 2018-05-15

引用本文:

牟伟杰1.2，石林锁1，蔡艳平1，郑勇1，刘浩1. 基于振动时频图像全局和局部特征融合的柴油机故障诊断[J]. 振动与冲击, 2018, 37(10): 14-19.
MU Weijie1.2, SHI Linsuo1, CAI Yanping1, ZHENG Yong1, LIU Hao1. Diesel engine fault diagnosis based on the global and local features fusion of timefrequency image. JOURNAL OF VIBRATION AND SHOCK, 2018, 37(10): 14-19.

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http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2018/V37/I10/14

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