|Table of Contents|

[1] Mao Yifan, Xu Feiyun,. A fault feature extraction method of gearbox based on compounddictionary noise reduction and optimized Fourier decomposition [J]. Journal of Southeast University (English Edition), 2021, (1): 22-32. [doi:10.3969/j.issn.1003-7985.2021.01.004]

A fault feature extraction method of gearbox based on compounddictionary noise reduction and optimized Fourier decomposition()

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Research Field:
Mechanical Engineering
Publishing date:


A fault feature extraction method of gearbox based on compounddictionary noise reduction and optimized Fourier decomposition
Mao Yifan Xu Feiyun
School of Mechanical Engineering, Southeast University, Nanjing 211189, China
Fourier decomposition compound dictionary mode mixing gearbox fault feature extraction
Aimed at the problem that Fourier decomposition method(FDM)is sensitive to noise and existing mode mixing cannot accurately extract gearbox fault features, a gear fault feature extraction method combining compound dictionary noise reduction and optimized FDM(OFDM)is proposed. Firstly, the characteristics of the gear signals are used to construct a compound dictionary, and the orthogonal matching pursuit algorithm(OMP)is combined to reduce the noise of the vibration signal. Secondly, in order to overcome the mode mixing phenomenon occuring during the decomposition of FDM, a method of frequency band division based on the extremum of the spectrum is proposed to optimize the decomposition quality. Then, the OFDM is used to decompose the signal into several analytic Fourier intrinsic band functions(AFIBFs). Finally, the AFIBF with the largest correlation coefficient is selected for Hilbert envelope spectrum analysis. The fault feature frequencies of the vibration signal can be accurately extracted. The proposed method is validated through analyzing the gearbox fault simulation signal and the real vibration signals collected from an experimental gearbox.


[1] Lu S L, He Q B, Wang J. A review of stochastic resonance in rotating machine fault detection[J].Mechanical Systems and Signal Processing, 2019, 116: 230-260. DOI:10.1016/j.ymssp.2018.06.032.
[2] Li N, Huang W G, Guo W J, et al. Multiple enhanced sparse decomposition for gearbox compound fault diagnosis[J].IEEE Transactions on Instrumentation and Measurement, 2020, 69(3): 770-781. DOI:10.1109/TIM.2019.2905043.
[3] Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J].Proceedings of the Royal Society of London Series A: Mathematical, Physical and Engineering Sciences, 1998, 454(1971): 903-995. DOI:10.1098/rspa.1998.0193.
[4] Wu Z H, Huang N E. Ensemble empirical mode decomposition: A noise-assisted data analysis method[J].Advances in Adaptive Data Analysis, 2009, 1(1): 1-41. DOI:10.1142/s1793536909000047.
[5] Smith J S. The local mean decomposition and its application to EEG perception data[J].Journal of the Royal Society, Interface, 2005, 2(5): 443-454. DOI:10.1098/rsif.2005.0058.
[6] Yang Y, Cheng J S, Zhang K. An ensemble local means decomposition method and its application to local rub-impact fault diagnosis of the rotor systems[J].Measurement, 2012, 45(3): 561-570. DOI:10.1016/j.measurement.2011.10.010.
[7] Gilles J. Empirical wavelet transform[J].IEEE Transactions on Signal Processing, 2013, 61(16): 3999-4010. DOI:10.1109/TSP.2013.2265222.
[8] Dragomiretskiy K, Zosso D. Variational mode decomposition[J].IEEE Transactions on Signal Processing, 2014, 62(3): 531-544. DOI:10.1109/TSP.2013.2288675.
[9] Singh P, Singh P, Joshi S D, et al. The Fourier decomposition method for nonlinear and non-stationary time series analysis[J].Proceedings Mathematical, Physical, and Engineering Sciences, 2017, 473(2199): 20160871. DOI:10.1098/rspa.2016.0871.
[10] Liu Y, Liu X B, Liang S, et al. Aeroengine rotor fault diagnosis based on Fourier decomposition method [J]. China Mechanical Engineering, 2019, 30(18): 2156-2163. DOI:10.3969/j.issn.1004-132X.2019.018.003. (in Chinese)
[11] Deng M Q, Deng A D, Zhu J, et al. Bandwidth Fourier decomposition and its application in incipient fault identification of rolling bearings[J].Measurement Science and Technology, 2020, 31(1): 015012. DOI:10.1088/1361-6501/ab4069.
[12] Dou C H, Lin J S. Extraction of fault features of machinery based on Fourier decomposition method[J].IEEE Access, 2019, 7: 183468-183478. DOI:10.1109/ACCESS.2019.2960548.
[13] Singh P. Novel Fourier quadrature transforms and analytic signal representations for nonlinear and non-stationary time-series analysis[J]. Royal Society Open Science, 2018, 5(11): 181131. DOI: 10.1098/rsos.181131.
[14] Duarte M F, Davenport M A, Takhar D, et al. Single-pixel imaging via compressive sampling[J].IEEE Signal Processing Magazine, 2008, 25(2): 83-91. DOI:10.1109/MSP.2007.914730.
[15] Feng Z P, Zhou Y K, Zuo M J, et al. Atomic decomposition and sparse representation for complex signal analysis in machinery fault diagnosis: A review with examples[J].Measurement, 2017, 103: 106-132. DOI:10.1016/j.measurement.2017.02.031.
[16] Yan R Q, Gao R X, Chen X F. Wavelets for fault diagnosis of rotary machines: A review with applications[J].Signal Processing, 2014, 96: 1-15. DOI:10.1016/j.sigpro.2013.04.015.
[17] Chen X F, Du Z H, Li J M, et al. Compressed sensing based on dictionary learning for extracting impulse components[J].Signal Processing, 2014, 96: 94-109. DOI:10.1016/j.sigpro.2013.04.018.
[18] Aharon M, Elad M, Bruckstein A. K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation[J].IEEE Transactions on Signal Processing, 2006, 54(11): 4311-4322. DOI:10.1109/TSP.2006.881199.
[19] Ophir B, Lustig M, Elad M. Multi-scale dictionary learning using wavelets[J].IEEE Journal of Selected Topics in Signal Processing, 2011, 5(5): 1014-1024. DOI:10.1109/JSTSP.2011.2155032.
[20] Medina R, Alvarez X, Jadán D, et al. Gearbox fault classification using dictionary sparse based representations of vibration signals[J].Journal of Intelligent & Fuzzy Systems, 2018, 34(6): 3605-3618. DOI:10.3233/jifs-169537.
[21] Nagaraj S B, Stevenson N, Marnane W, et al. A novel dictionary for neonatal EEG seizure detection using atomic decomposition[C]//2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. San Diego, CA, USA, 2012: 1073-1076. DOI:10.1109/EMBC.2012.6346120.
[22] Lü Y, Luo J, Yi C C. Enhanced orthogonal matching pursuit algorithm and its application in mechanical equipment fault diagnosis[J].Shock and Vibration, 2017, 2017: 1-13. DOI:10.1155/2017/4896056.
[23] Pati Y C, Rezaiifar R, Krishnaprasad P S. Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition[C]//Proceedings of 27th Asilomar Conference on Signals, Systems and Computers. Pacific Grove, CA, USA, 1993: 40-44. DOI:10.1109/ACSSC.1993.342465.
[24] Ding K, Li W, Zhu X. The useful technique of gear and gear faults diagnosis [M]. Beijing: China Machine Press, 2005: 35-38.(in Chinese)
[25] Antoni J. Fast computation of the kurtogram for the detection of transient faults[J]. Mechanical Systems and Signal Processing, 2007, 21(1): 108-124. DOI:10.1016/j.ymssp.2005.12.002.


Biographies: Mao Yifan(1993—), male, graduate; Xu Feiyun(corresponding author), male, doctor, professor, fyxu@seu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No. 51975117), the Key Research & Development Program of Jiangsu Province(No. BE2019086).
Citation: Mao Yifan, Xu Feiyun. A fault feature extraction method of gearbox based on compound dictionary noise reduction and optimized Fourier decomposition[J].Journal of Southeast University(English Edition), 2021, 37(1):22-32.DOI:10.3969/j.issn.1003-7985.2021.01.004.
Last Update: 2021-03-20