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Gear fault detection and diagnosis under speed-up condition based on order cepstrum and radial basis function neural network

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Abstract

Varying speed machinery condition detection and fault diagnosis are more difficult due to non-stationary machine dynamics and vibration. Therefore, most conventional signal processing methods based on time invariant carried out in constant time interval are frequently unable to provide meaningful results. In this paper, a study is presented to apply order cepstrum and radial basis function (RBF) artificial neural network (ANN) for gear fault detection during speedup process. This method combines computed order tracking, cepstrum analysis with ANN. First, the vibration signal during speed-up process of the gearbox is sampled at constant time increments and then is re-sampled at constant angle increments. Second, the re-sampled signals are processed by cepstrum analysis. The order cepstrum with normal, wear and crack fault are processed for feature extracting. In the end, the extracted features are used as inputs to RBF for recognition. The RBF is trained with a subset of the experimental data for known machine conditions. The ANN is tested by using the remaining set of data. The procedure is illustrated with the experimental vibration data of a gearbox. The results show the effectiveness of order cepstrum and RBF in detection and diagnosis of the gear condition.

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Authors and Affiliations

  1. Department of Electromechanical Engineering, Shijiazhuang Institute of Railway Technology, Shijiazhuang, 050041, P.R. China

    Hui Li & Yuping Zhang

  2. First Department, Shijiazhuang Mechanical Engineering College, Shijiazhuang, 050003, P.R. China

    Haiqi Zheng

Authors
  1. Hui Li
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  2. Yuping Zhang
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  3. Haiqi Zheng
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Corresponding author

Correspondence to Hui Li.

Additional information

This paper was recommended for publication in revised form by Associate Editor Hong Hee Yoo

Hui Li received his B.S. in Mechanical Engineering from the Hebei Polytechnic University, Hebei, China, in 1991. He received his M.S. in Mechanical Engineering from the Harbin University of Science and Technology, Heilongjiang, China, in 1994. He received his PhD from the School of Mechanical Engineering of Tianjin University, Tianjin, China, in 2003. He was a postdoctoral researcher in Shijiazhuang Mechanical Engineering College from August 2003 to September 2005, and in Beijing Jiaotong University from March 2006 to December 2008. He is currently a professor in Mechanical Engineering at Shijiazhuang Institute of Railway Technology, China. His research and teaching interests include hybrid driven mechanism, kinematics and dynamics of machinery, mechatronics, CAD/CAPP, signal processing for machine health monitoring, diagnosis and prognosis. He is currently a senior member of the Chinese Society of Mechanical Engineering.

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Li, H., Zhang, Y. & Zheng, H. Gear fault detection and diagnosis under speed-up condition based on order cepstrum and radial basis function neural network. J Mech Sci Technol 23, 2780–2789 (2009). https://doi.org/10.1007/s12206-009-0730-8

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  • DOI: https://doi.org/10.1007/s12206-009-0730-8

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