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

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Journal of Vibration and Shock ›› 2018, Vol. 37 ›› Issue (10) : 14-19.

MU Weijie1.2, SHI Linsuo1, CAI Yanping1, ZHENG Yong1, LIU Hao1

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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

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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[J]. Journal of Vibration and Shock, 2018, 37(10): 14-19

References

[1] 张云强,张培林,吴定海,李兵. 基于最优广义S变换和脉冲耦合神经网络的轴承故障诊断[J]. 振动与冲击, 2015,34(9):26-31.
Zhang Yunqiang, Zhang Peilin, Wu Dinghai, et al. Bearing fault diagnosis based on optimal generalized S transform and pulse coupled neural network[J]. Journal of vibration and shock, 2015, 34(9):26-31.
[2] 李巍华, 林龙, 单外平. 基于广义S变换与双向2DPCA的轴承故障诊断[J]. 振动、测试与诊断, 2015, 35（3）:499-592.
Li Weihua,Lin Long,Shan Waiping. Bearing Fault Diagnosis Based on Generalized S-Transform and Two-Directional 2DPCA[J]. Journal of Vibration, Measurement & Diagnosis, 2015,35(3):499-592.
[3] 林龙. 基于S变换和图像纹理信息的轴承故障智能诊断方法[J]. 科学技术与工程, 2014, 14(06): 26-30.
Lin L. An Intelligent Bearing Fault Diagnosis Method Based on S Transform and Texture Image[J]. Science Technology and Engineering, 2014, 14(06): 26-30.
[4] Ali MD.Younus, Bo-Suk Yang. Intelligent fault diagnosis of rotating machinery using infrared thermal image[J]. Expert Systems with Application, 2012, 39(2): 2082-2091.
[5] Fadel M. Megahed, Jaime A. Camelio. Real-time fault detection in manufacturing environments using face recognition techniques[J].Journal of Intelligent Manufacturing, 2012,23(3):393-408.
[6] Hyunuk Ha,Sunsin Han,Jangmyung Lee et al. Fault Detection on Transmission Lines Using a Microphone Array and an Infrared Thermal Imaging Camera[J].IEEE Transactions on Instrumentation and Measurement,2012,61(1):267-275.
[7] 沈虹,赵红东,梅检民,曾锐利. 基于高阶累积量图像特征的柴油机故障诊断研究[J]. 振动与冲击, 2015, 34(11): 133-138.
Shen H, Zhao H D, Mei J M, et al. Diesel engine fault diagnosis based on high-order cumulant image features[J]. Journal of vibration and shock, 2015, 34(11): 133-138.
[8] 夏勇，商斌梁，张振仁. 基于小波包与图像处理的内燃机故障诊断研究[J].内燃机学报，2001，19(1)：62-68.
XIA Y, SHANG B L, ZHANG Z R,et al. Research on fault diagnosis for internal combustion engines using wavelet packet and image processing[J].Transaction of CSICE, 2001，19(1)：62-68.
[9] 王成栋，张优云，夏勇. 基于 S 变换的柴油机气门机构故障诊断研究[J].内燃机学报，2003，21(4)：271-275.
Wang C D, Zhang Y Y, Xia Y. Fault diagnosis for diesel valve train based on S Transform[J]. Transaction of CSICE, 2003，21(4):271-275.
[ 0] 王成栋，张优云，夏勇. 模糊函数图像在柴油机气门故障诊断中的应用研究[J].内燃机学报，2004，22(4)：162-168.
Wang C D, Zhang Y Y, Xia Y. Study on the use of ambiguity function images in fault diagnosis for diesel valve train[J]. Transaction of CSICE, 2004，22(4)：162-168.
[ 1] 蔡艳平，李艾华，王涛，等. 基于时频谱图与图像分割的柴油机故障诊断[J]. 内燃机学报, 2011, 29 (2): 181-186.
Cai Y P, Li A H,Wang T,et al. I.C. Diesel Engine Fault Diagnosis Based on Time-Frequency Spectrum Image and Image Segmentation [J]. Transactions of CSICE, 2011，29(2)：181-186．
[ 2] 蔡艳平，李艾华，石林锁，等. 基于 EMD-WVD 振动谱图像 SVM 识别的内燃机故障诊断[J]. 内燃机工程, 2012, 33(2):72-78.
Cai Y P, Li A H, Shi L S, et al. IC Engine fault diagnosis method based on EMD-WVD Vibration Spectrum time-frequency image recognition by SVM[J]. Chinese internal combustion engine engineering, 2012, 33(2):72-78.
[13] Scholkopf B, Smola A. Nonlinear Component Analysis as a Kernel Eigenvalue problem[J]. Neural computation, 1998,10(5):1299-1319.
[ 4] 刘昱昊.基于非负矩阵分解算法的人脸识别技术的研究[D]. 吉林：吉林大学，2014．
Liu Y H. Research on NMF-based Algorithms applying to face recognition [D].Ji Lin: Ji Lin University,2014．
[ 5] 袁宝华, 王欢，任明武. LBP与LNMF特征融合的人脸识别[J].计算机工程与应用, 2013, 49(5):166-169.
YUAN B H,WANG H, Ren M W.Fusing local binary pattern and LNMF of face recognition[J].Computer Engineering and Application, 2013, 49(5):166-169.
[16] 向阳辉，张干清，庞佑霞.结合SVM和改进证据理论的多信息融合故障诊断[J].振动与冲击，2015，34（13）：71-77.
XIANG Y H, ZHANG G Q, PANG Y X. Multi-information fusion fault diagnosis using SVM & improved evidence theory[J]. Journal of Vibration and Shock, 2015，34（13）：71-77.
[17] Hyvarinen A, Oja E. Independent component analysis: algorithm and application[J]. Neural Networks, 2000(13): 411-430.