Diesel engine fault diagnosis based on the global and local features fusion of time-frequency image

被引：0
|
作者：
Mu W. [1 ,2 ]
Shi L. [1 ]
Cai Y. [1 ]
Zheng Y. [1 ]
Liu H. [1 ]
机构：
[1] Rocket Force University of Engineering, Xi'an
[2] PLA Army Special Operations College of Engineering, Guilin
来源：
Zhendong yu Chongji/Journal of Vibration and Shock | 2018年 / 37卷 / 10期
关键词：
Diesel; Fault diagnosis; Features fusion; Global feature; Local feature; Time-frequency image;
D O I：
10.13465/j.cnki.jvs.2018.10.003
中图分类号：
学科分类号：
摘要：
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. © 2018, Editorial Office of Journal of Vibration and Shock. All right reserved.
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页码：14 / 19and49
页数：1935
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