Research on diesel engine piston wear fault diagnosis method based on the local wave time-frequency analysis

被引：0

作者：

Zhao, Fengqiang ^{[1
,2
]}

Li, Guangqiang ^{[1
,3
]}

Hu, Hongying ^{[2
]}

Du, Jialu ^{[1
]}

Guo, Chen ^{[1
]}

Li, Tao ^{[1
]}

Fu, Xinwen ^{[3
]}

机构：

[1] School of Information Science and Technology, Dalian Maritime University, Dalian

[2] College of Electromechanical and Information Engineering, Dalian Nationalities University, Dalian

[3] Department of Computer Science, University of Massachusetts Lowell, Lowell, 01854, MA

来源：

Open Automation and Control Systems Journal | 2014年 / 6卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Diesel; Fault diagnosis; Local wave method; Time-frequency analysis;

D O I：

10.2174/1874444301406010913

中图分类号：

学科分类号：

摘要：

A large number of non-stationary dynamic signals are generated in the working machinery and equipment. Especially, diesel engines often encounter non-stationary transient and time-varying modulation signals, such as the impulse response signals caused by cylinder piston wear. These kinds of signals generated from diesel engine are analyzed by the method of the Local Wave time-frequency proposed in this paper, and then according to the analysis to diagnose the working state of the diesel engine. It proved that the proposed method is feasible and effective. Moreover, it provides an effective way for the diesel engine condition monitor and fault diagnosis. © Zhao et al.; Licensee Bentham Open.

引用

页码：913 / 918

页数：5

共 10 条

[1]

Ma X., Yu B., Cai Y., A new time-frequency analysis method - local wave method, Journal of Vibration Engineering, 13, S, pp. 219-224, (2000)

[2]

Liu Y., Tan Y., Xie H., Wang W., Gao Z., Time-frequency analysis of non-stationary fusion plasma signals using an improved Hilbert-Huang transform, Review of Scientific Instruments, 85, 7, (2014)

[3]

Orovic I., Draganic A., Stankovic S., Sejdic E., A unified approach for the estimation of instantaneous frequency and its derivatives for non-stationary signals analysis, 11Th International Conference on Information Science, Signal Processing and Their Applications, pp. 1130-1134, (2012)

[4]

Zou Y., Research and Application on the Theory and Method of Local Wave Analysis, (2004)

[5]

Gai Q., Ma X., Zhang H., Comparing study for decomposing methods using local wave method, Systems Engineering and Electronics, 24, 2, pp. 57-59, (2002)

[6]

Huang N.E., Shen Z., Long S.R., Wu M.C., Shih H.H., Zheng Q., Yen N., Tung C.C., Liu H.H., The empirical mode decomposition and the hilbert spectrum for nonlinear non-stationary time series analysis, Proceedings a Royal Society London, 471, 2175, pp. 903-995, (1998)

[7]

Yu B., Adaptive Time-Frequency Analysis Method and Its Applications in Fault Diagnosis, (1998)

[8]

Song B.L., The research on fault diagnosis of diesel engine based on EMD, Applied Mechanics and Materials, 623, pp. 143-148, (2014)

[9]

Wang X., Liu C., Bi F., Bi X., Shao K., Fault diagnosis of diesel engine based on adaptive wavelet packets and EEMD-fractal dimension, Mechanical Systems and Signal Processing, 41, 1-2, pp. 581-597, (2013)

[10]

Feng L., Feng F., Wavelet transform technology in the nonstationary signal fault diagnosis in engineering application, Advanced Materials Research, 518-523, pp. 1355-1358, (2012)

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