Feature Extraction and Fault Diagnosis Based on FDM and RCMDE

被引：0
作者：
Zuo H. [1 ,2 ]
Liu X. [1 ,2 ]
Hong L. [1 ,2 ]
机构：
[1] School of Aeronautical Manufacturing Engineering, Nanchang Hangkong University, Nanchang
[2] School of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing
来源：
Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis | 2021年 / 41卷 / 03期
关键词：
Fault diagnosis; Fourier decomposition method; Refined composite multiscale dispersion entropy; Two-stage adaptive wavecluster;
D O I：
10.16450/j.cnki.issn.1004-6801.2021.03.017
中图分类号：
学科分类号：
摘要：
Feature extraction is particularly important for diagnosing the faults of aero-engine rotor. Based on the nonlinearity and nonstationarity of vibration signal of aero-engine rotor, first of all, Fourier decomposition method (FDM) is applied to signal of aero-engine rotor. The marginal spectrum centroid and the power spectral centroid of maximum energy layer are extracted. Then the refined composite multiscale dispersion entropy (RCMDE) of vibration signal is calculated. Finally, the method of two-stage adaptive wavecluster is applied to fault classification and recognition of eigenvector space. Through the sample verification of the aero-engine rotor test rig, it is shown that the extracted characteristic vectors are accurate and fluctuated little, the value of the same fault type is concentrated, and the difference between different fault types is large, which are helpful to improve the diagnosis accuracy of multiple-faults. © 2021, Editorial Department of JVMD. All right reserved.
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页码：539 / 546
页数：7
相关论文
共 12 条
  • [1] ZHANG X Y, LIANG Y T, ZHOU J H, Et al., A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM, Measurement, 69, pp. 164-179, (2015)
  • [2] LI Ruitong, WANG Huaqing, QU Hongwei, Et al., Application of CICA in compound fault feature extracting of rolling bearings, Noise & Vibration Control, 35, 3, pp. 173-176, (2015)
  • [3] WANG Y X, MARKERT R, XIANG J W, Et al., Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system, Mechanical Systems & Signal Processing, 60, 61, pp. 243-251, (2015)
  • [4] WAN Shuting, DOU Longjiang, LI Cong, Et al., Fault feature extraction and classification of high voltage circuit breakers based on VMD and sample entropy, Journal of Vibration & Shock, 37, 20, pp. 32-38, (2018)
  • [5] YANG Dawei, FENG Fuzhou, ZHAO Yongdong, Et al., A VMD sample entropy feature extraction method and its application in planetary gearbox fault diagnosis, Journal of Vibration & Shock, 37, 16, pp. 198-205, (2018)
  • [6] ZHENG Jinde, CHENG Junsheng, YANG Yu, Multi-scale permutation entropy and its applications to rolling bearing fault diagnosis, China Mechanical Engineering, 24, 19, pp. 2641-2646, (2013)
  • [7] ZHANG Shuqing, SUN Guoxiu, LI Liang, Et al., Study on mechanical fault diagnosis method based on LMD approximate entropy and fuzzy C-means clustering, Chinese Journal of Scientific Instrument, 34, 3, pp. 714-720, (2013)
  • [8] ROSTAGHI M, AZAMI H., Dispersion entropy: a measure for time series analysis, IEEE Signal Processing Letters, 23, 5, pp. 610-614, (2016)
  • [9] AZAMI H, ROSTAGHI M, ABASOLO D, Et al., Refined composite multiscale dispersion entropy and its application to biomedical signals, IEEE Transactions on Biomedical Engineering, 64, 12, pp. 2872-2879, (2017)
  • [10] SINGH P, JOSHI S D, PATNEY R K, Et al., The Fourier decomposition method for nonlinear and nonstationary time series analysis, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 473, 2199, pp. 871-885, (2017)
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