The order analysis method based on the adaptive and sparsest time-frequency analysis method and its application

被引：0
|
作者：
Cheng J.-S. [1 ]
Li B.-Q. [1 ]
Peng Y.-F. [1 ]
Wu Z.-T. [1 ]
Yang Y. [1 ]
机构：
[1] State Key Laboratory of Advanced Design and Manufacture for Vehicle Body, Hunan University, Changsha
来源：
| 1600年 / Nanjing University of Aeronautics an Astronautics卷 / 29期
关键词：
Adaptive and sparsest time-frequency analysis; Fault diagnosis; Gear; Non-stationary time-varying signal; Order analysis;
D O I：
10.16385/j.cnki.issn.1004-4523.2016.03.021
中图分类号：
学科分类号：
摘要：
Adaptive and sparsest time-frequency analysis (ASTFA) is a new analysis method, in which the signal decomposition problem is translated into optimization problem, and the signal can be decomposed adaptively in the optimization. In order to solve the problem of selecting the initial phase function of ASTFA method, an improved ASTFA method is proposed, based on which a method called order analysis is proposed. In this method, the original signal is decomposed by the improved ASTFA method to obtain the instantaneous amplitude of the components, and then the instantaneous amplitude is analyzed by order analysis method to extract the fault feature information. The method is applied to the analysis of the non-stationary time-varying signal which is produced in the process of variable speed gearing. Simulation analysis and experimental analysis show that the order analysis method based on ASTFA can accurately extract the fault feature information of the gear with certain advantages. © 2016, Nanjing Univ. of Aeronautics an Astronautics. All right reserved.
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页码：542 / 548
页数：6
相关论文
共 11 条
  • [1] Chen X., Yu D., Li R., A compound faults method for variational-speed gearbox based on order tracking demodulation spectrum, Journal of Vibration Engineering, 26, 6, pp. 951-959, (2013)
  • [2] Kang H., Luan J., Zheng H., Et al., Envelope demodulation analysis of bearing based on order tracking and empirical mode decomposition, Journal of Mechanical Engineering, 43, 8, pp. 119-122, (2007)
  • [3] Huang N.E., Shen Z., Long S.R., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proceedings of the Royal Society A, 454, pp. 903-995, (1998)
  • [4] Cheng J., Li B., Yang Y., Et al., Application of the envelope order spectrum based on the generalized demodulation time-frequency analysis to the gear fault diagnosis, Journal of Vibration Engineering, 22, 5, pp. 467-473, (2009)
  • [5] Yang Y., Yu D., Cheng J., Roller bearing fault diagnosis method based on EMD, China Mechanical Engineering, 15, 10, pp. 908-920, (2004)
  • [6] Lei Y.G., Lin J., He Z.J., Et al., A review on empirical mode decomposition in fault diagnosis of rotating machinery, Mechanical Systems and Signal Processing, 35, 1-2, pp. 108-126, (2013)
  • [7] Ren D., Yang S., Wu Z., Et al., Comparison of instantaneous frequency directed computing method and Hilbert transform and Teager energy method, Journal of Mechanical Engineering, 49, 9, pp. 42-48, (2013)
  • [8] Hou T.Y., Shi Z.Q., Adaptive data analysis via sparse time-frequency representation, Advances in Adaptive Data Analysis, 3, 1-2, pp. 1-28, (2011)
  • [9] Hou T.Y., Shi Z.Q., Data-driven time-frequency analysis, Applied and Computational Harmonic Analysis, 35, 2, pp. 284-308, (2013)
  • [10] Hou T.Y., Shi Z.Q., Tavallali P., Convergence of a data-driven time-frequency analysis method, Applied and Computational Harmonic Analysis, 37, 2, pp. 235-270, (2014)
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