The Mechanical Fault Diagnosis Method Based on EMD and Time Series Analysis

Jun Ma; Shi Hai Zhang

doi:10.4028/www.scientific.net/AMM.602-605.2330

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 602-605The Mechanical Fault Diagnosis Method Based on EMD...

The Mechanical Fault Diagnosis Method Based on EMD and Time Series Analysis

Abstract:

It is the precondition of vibration fault diagnosis technology that appropriate signal analysis method is applied to separate mechanical fault character message from vibration monitoring signal. Based on the characteristics of multi-exciting, multi-model, non-stationary, nonlinear of the complex mechanical vibration signal, the EMD (Empirical Mode Decomposition) method is firstly applied to decompose the most refined IMF (Intrinsic Mode Function) components of vibration monitoring signal, and then the time series analysis method is applied to estimate power spectrums of IMF components and separate the fault character messages. The feasibility and advantage of the associated method are proved by analyzing the diesel engine crankshaft vibration monitoring signal in the paper.

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Periodical:

Applied Mechanics and Materials (Volumes 602-605)

Pages:

2330-2333

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.602-605.2330

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Online since:

August 2014

Authors:

Jun Ma*, Shi Hai Zhang

Keywords:

Empirical Mode Decomposition (EMD), Fault Diagnosis, Power Spectrum, Time Series Analysis

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* - Corresponding Author

References

[1] Li Guohua, Zhang Yongzhong. Mechanical Fault Diagnosis. Chemical Industry Press. Beijing, 2011, 1.

Google Scholar

[2] Zhang Shihai. Identification of diesel engine behavior based on the vibration signal analysis of cylinder cover. Ship Engineering. 2009(31): 24-26.

Google Scholar

[3] Huang N. E, etal. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc.R. Soc. Lond.A. 1998(454): 903-995.

DOI: 10.1098/rspa.1998.0193

Google Scholar

[4] Weiqin Yang, Lan Gu. Time Series Analysis and Dynamic Data Simulation. Beijing Institute of Technology Press. Beijing, 1986. 12.

Google Scholar