Abstract
In this paper, a new method for fault detection of parallel shaft gearbox based on the Empirical Mode Decomposition (EMD) and Multipoint Optimal Minimum Entropy Deconvolution (MOMEDA) is proposed. MOMEDA can overcome the shortcomings of minimum entropy deconvolution (MED) and Maximum Correlated Kurtosis Deconvolution (MCKD), and it is introduced to extract the fault cycle of gearbox signals. The vibration signals of gearbox are complex, including fault signals, noise signals and deterministic signals such as gear meshing component. Fault signal is often buried in these other components, which increases the difficulty of gearbox fault detection. Thus the EMD is proposed to decompose the signal and extract the fault impact components from the signal. The parallel shaft gearbox preset fault experiment is carried out to verify the effectiveness of method. In addition, some traditional methods, such as Fourier transform, cepstrum analysis, MED and MCKD, are used to compare with the proposed methods. Experimental results show that the effectiveness of the proposed method is better than that of traditional methods.
Similar content being viewed by others
References
Cabrelli CA (1984) Minimum entropy deconvolution and simplicity: a noniterative algorithm. Geophysics 50:394–413
Cabrelli CA (1985) Minimum entropy deconvolution and simplicity: a noniterative algorithm. Geophysics 50(3):394–413
Cheng J, Dejie Yu, Yang Yu (2006) A fault diagnosis approach for roller bearing based onEMD method and AR model. Mech Syst Signal Process 20:350–362
Endo H, Randall RB (2007) Enhancement of autoregressive model based gear tooth fault detection technique by the use of minimum entropy deconvolution filter. Mech Syst Signal Process 21:906–919
Endo H, Randall RB, Gosselin C (2009) Differential diagnosis of spall cracks in the gear tooth fillet region: experimental validation. Mech Syst Signal Process 23:636–651
Feng Z, Chen X, Liang M (2016) Joint envelope and frequency order spectrum analysis based on iterative generalized demodulation for planetary gearbox fault diagnosis under non-stationary conditions. Mech Syst Signal Process 76:242–264
Golafshan R, Sanliturk KY (2015) SVD and Hankel matrix based de-noising approach for ball bearing fault detection and its assessment using artificial faults. Mech Syst Signal Process 70–71:36–50
Gu D, Kim JG, An YS, Choi BK (2011) Detection of faults in gearboxes using acoustic emission signal. J Mech Sci Technol 25(5):1279–1286
Halim EB, Shoukat Choudhury MAA, Shan SL, Zuo MJ (2008) Time domain averaging across all scales: a novel method for detection of gearbox faults. Mech Syst Signal Process 22:261–278
Hamilton A, Quail DF (2011) Detailed state of the art review for the different on-line/in-line oil analysis techniques in context of wind turbine gearboxes. ASME J Tribol 133(4):1–17
Hong L, Dhupia JS (2014) A time domain approach to diagnose fearbox fault based on measured vibration signals. J Sound Vib 333:2164–2180
Hong L, Dhupia JS, Sheng S (2014) An explanation of frequency features enabling detection of faults in equally spaced planetary gearbox. Mech Mach Theory 73:169–183
Honorio BCZ, Mmond RD, Vidal AC, Leite EP (2012) Well log denoising and geological enhancement based on discrete wavelet transform and hybrid thresholding. Energy Explor Exploit 30:417–433
Huang NE, Shen Z, Long SR et al (1998) The empirical mode decomposition and the Hilbert spectrum for non-linear and non-stationary time series analysis. Proc R Soc Lond A 454:903–995
Jiang R, Chen J, Dong G et al (2012) The weak fault diagnosis and condition monitoring of rolling element bearing using minimum entropy deconvolution and envelop spectrum. J Mech Eng Sci 227:1116–1129
Li B, Zhang X, Jili W (2017) New procedure for gear fault dectection and diagnosis using instantaneous angular speed. Mech Syst Signal Process 85:415–428
Lim GM, Bae DM, Kim JH (2014) Fault diagnosis of rotating machine by thermography method on support vector machine. J Mech Sci Technol 28(8):2947–2952
McDonald GL, Zhao Q (2017) Multipoint optimal minimum entropy deconvolution and convolution fix: application to vibration fault detection. Mech Syst Signal Process 82:461–477
McDonald GL, Zhao Q, Zuo MJ (2012) Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection. Mech Syst Signal Process 33:237–255
McFadden PD (1991) A Technique for calculating the time domain averages of the vibration of the individual planet gears and the sun gear in an epicyclic gearbox. J Sound Vib 144(1):163–172
McFadden PD, Smith JD (1985) A signal processing technique for detecting local defects in a gear from the signal average of the vibration. Proc Inst Mech Eng Part C J Mech Eng Sci 199(43):287–292
Obuchowski J, Zimroz R, Wylomanska A (2016) Blind equalization using combined skewness-kurtosis criterion for gearbox vibration enhancement. Measurement 88:34–44
Peng ZK, Tse PW, Chu FL (2005) An improved Hilbert-Huang transform and its application in vibration signal analysis. J Sound Vib 286:187–205
Samuel PD, Pines DJ (2005) A review of vibration-based techniques for helicopter transmission diagnostics. J Sound Vib 282:475–508
Sawalhi N, Randall RB, Endo H (2007) The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis. Mech Syst Signal Process 21:2616–2633
Tang BP, Dong SJ, Song T (2012) Method for eliminating mode mixing of empirical mode decomposition based on revised blind source separation. Signal Process 92:248–258
Wiggins RA (1978) Minimum entropy deconvolution. Geoexploration 16(1–2):21–35
Wu ZH, Huang NE (2009) Ensemble empirical mode decomposition: a noise assisted data analysis method. Adv Adapt Data Anal 13:1–41
Yang WX (2008) Interpretation of mechanical signals using an improved Hilbert–Huang transform. Mech Syst Signal Process 22:1061–1071
Yang Yu, Dejie Yu, Cheng J (2006) A roller bearing fault diagnosis method based on EMD energy entropy and ANN. J Sound Vib 294:269–277
Yu K, Lin TR, Tan JW (2017) A bearing fault diagnosis technique based on singular values of EEMD spatial condition matrix and Gath-Geva clustering. Appl Acoust 121:33–45
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Zhang, X., Zhao, J., Ni, X. et al. Fault diagnosis for gearbox based on EMD-MOMEDA. Int J Syst Assur Eng Manag 10, 836–847 (2019). https://doi.org/10.1007/s13198-019-00818-5
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13198-019-00818-5