System Modeling and Application in Gearbox Fault Diagnosis Based on EMD and ARX Model

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Abstract:

The EMD method as a means of signal pre-processing is used to decompose the vibration signal of gearbox into a number of IMF components, using the largest energy component as an effective component of the output response signal and the input shaft rotate speed or torque signal as the system input signal, then the time-series ARX model is established, in this paper. Extracted model parameters are as a feature vector of recognized working state of gearbox, and as input parameters of RBF neural network to achieve automatic recognition of faults in the gearbox. The results show that: ARX model can enhance the anti-jamming capability of model, and have great significance for improving the accuracy of fault diagnosis by calculating the fluctuations of input signal into the model.

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532-536

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October 2011

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] GaoYongsheng, Tang Liwei and Wang Jianhua, etc.: Fault Diagnosis of Gearbox Based on its System Performance. Coal Mine Machinery, Vol. 27-1 (2006), pp.164-166.

Google Scholar

[2] G. Rilling,P. Flandrin and P. Goncalves: Empirical Mode Decomposition As a Filter Bank. IEEE Signal Processing Letters, Vol. 11-2 (2004), pp.112-114.

DOI: 10.1109/lsp.2003.821662

Google Scholar

[3] Jin Haiwei: Gearbox modeling based on ARX model. Journal of Vibration and Shock, Vol. 30-1 (2011), pp.230-233.

Google Scholar

[4] Yang Shuzi, Wu ya and Xuan Jianping: Time Series Analysis in Engineering Application. Huazhong university of science and technology press, Wuhan, (2007).

Google Scholar

[5] Chen Jin, Liao Mingfu and Chen Gang: Signal periodic segment processing technique for diagnosing gear pairs faults. Journal of Aerospace Power, Vol. 8-21 (2006), pp.727-731.

Google Scholar

[6] Ni Boyi, Xiao Deyun: System Identification and Simulation Toolbox under MATLAB Environment. Journal of System Simulation, Vol. 18-6 (2006), pp.1493-1496.

Google Scholar