Amplitude demodulation is a key for diagnosing bearing faults. The quality of the demodulation determines the efficiency of the spectrum analysis in detecting the defect. A signal analysis technique based on minimum entropy deconvolution (MED), empirical mode decomposition (EMD), and Teager Kaiser energy operator (TKEO) is presented. The proposed method consists in enhancing the signal by using MED, decomposing the signal in intrinsic mode functions (IMFs) and selects only the IMF which presents the highest correlation coefficient with the original signal. In this study the first IMF1 was automatically selected, since it represents the contribution of high frequencies which are first excited at the early stages of degradation. After that, TKEO is used to track the modulation energy. The spectrum is applied to the instantaneous amplitude. Therefore, the character of the bearing faults can be recognized according to the envelope spectrum. The simulation and experimental results show that an envelope spectrum analysis based on MED-EMD and TKEO provides a reliable signal analysis tool. The experimental application has been developed on acoustic emission and vibration signals recorded for bearing fault detection. 1. Introduction The bearing may be considered as one of the most stressed parts in rotating machines. Early stage bearing defects excite first the resonance frequencies which manifest in the high frequency domain. High frequency resonance technique (HFRT) is thus mostly used in industry since it allows the extraction of components information representing defects on rotating machinery [1]. A band-pass filtering around the excited resonance frequency followed by an amplitude demodulation step exhibits the modulation frequencies representative of the fault characteristic frequency of the bearing and its associated harmonics [2, 3]. Spectral analysis of this signal (amplitude and number of harmonics) can reveal the severity of defects [4]. However, the major challenge in the application of the HFRT technique is the proper selection of the center frequency and bandwidth of the band-pass filter. Many researches have focused on the development of efficient and robust methods for estimating the proper center frequency and optimum bandwidth of the band-pass filter. Spectral Kurtosis has been proposed by Antoni and Randall [5]. However Kurtosis has its own limitation, especially when the signal is submerged by a strong and non-Gaussian noise with sudden high peaks where kurtosis shows extremely high values [6]. Other methods were developed. Barszcz and
References
[1]
P. D. McFadden and J. D. Smith, “Vibration monitoring of rolling element bearings by the high-frequency resonance technique—a review,” Tribology International, vol. 17, no. 1, pp. 3–10, 1984.
[2]
P. W. Tse, Y. H. Peng, and R. Yam, “Wavelet analysis and envelope detection for rolling element bearing fault diagnosis—their effectiveness and flexibilities,” Journal of Vibration and Acoustics, Transactions of the ASME, vol. 123, no. 3, pp. 303–310, 2001.
[3]
J. Altmann and J. Mathew, “Multiple band-pass autoregressive demodulation for rolling-element bearing fault diagnosis,” Mechanical Systems and Signal Processing, vol. 15, no. 5, pp. 963–977, 2001.
[4]
M. Thomas, Reliability, Predictive Maintenance and Machinery Vibration, Presses de l'Université du Québec, 2011, (French).
[5]
J. Antoni and R. B. Randall, “The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines,” Mechanical Systems and Signal Processing, vol. 20, no. 2, pp. 308–331, 2006.
[6]
Y. Imaouchen, M. Thomas, and R. Alkama, “Detection of bearing defects by combining Hilbert Transform to Kurtogram,” in Proceedings of the 2nd International Conference on Maintenance, Gestion, Logistic and Electrotechnic (CIMGLE '12), pp. 1–6, Oran, Algeria, November 2012.
[7]
T. Barszcz and A. Jab？oński, “A novel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram,” Mechanical Systems and Signal Processing, vol. 25, no. 1, pp. 431–451, 2011.
[8]
H. Qiu, J. Lee, J. Lin, and G. Yu, “Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics,” Journal of Sound and Vibration, vol. 289, no. 4-5, pp. 1066–1090, 2006.
[9]
H. Qiu, J. Lee, J. Lin, and G. Yu, “Robust performance degradation assessment methods for enhanced rolling element bearing prognostics,” Advanced Engineering Informatics, vol. 17, no. 3-4, pp. 127–140, 2003.
[10]
N. G. Nikolaou and I. A. Antoniadis, “Demodulation of vibration signals generated by defects in rolling element bearings using complex shifted Morlet wavelets,” Mechanical Systems and Signal Processing, vol. 16, no. 4, pp. 677–694, 2002.
[11]
J. Lin and M. J. Zuo, “Gearbox fault diagnosis using adaptive wavelet filter,” Mechanical Systems and Signal Processing, vol. 17, no. 6, pp. 1259–1269, 2003.
[12]
J. F. Kaiser, “On a simple algorithm to calculate the 'energy' of a signal,” in Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, pp. 381–384, April 1990.
[13]
A. Potamianos and P. Maragos, “A comparison of the energy operator and the Hilbert transform approach to signal and speech demodulation,” Signal Processing, vol. 37, no. 1, pp. 95–120, 1994.
[14]
P. Maragos, J. F. Kaiser, and T. F. Quatieri, “On amplitude and frequency demodulation using energy operators,” IEEE Transactions on Signal Processing, vol. 41, no. 4, pp. 1532–1550, 1993.
[15]
P. Maragos, J. F. Kaiser, and T. F. Quatieri, “Energy separation in signal modulations with application to speech analysis,” IEEE Transactions on Signal Processing, vol. 41, no. 10, pp. 3024–3051, 1993.
[16]
C. Junsheng, Y. Dejie, and Y. Yu, “The application of energy operator demodulation approach based on EMD in machinery fault diagnosis,” Mechanical Systems and Signal Processing, vol. 21, no. 2, pp. 668–677, 2007.
[17]
Z. Yuping, L. Hui, and B. Lihong, “Adaptive instantaneous frequency estimation based on EMD and TKEO,” in Proceedings of the 1st International Congress on Image and Signal Processing (CISP '08), pp. 60–64, May 2008.
[18]
M. Liang and I. S. Bozchalooi, “An energy operator approach to joint application of amplitude and frequency-demodulations for bearing fault detection,” Mechanical Systems and Signal Processing, vol. 24, no. 5, pp. 1473–1494, 2010.
[19]
G. F. Bin, J. J. Gao, X. J. Li, and B. S. Dhillon, “Early fault diagnosis of rotating machinery based on wavelet packets—empirical mode decomposition feature extraction and neural network,” Mechanical Systems and Signal Processing, vol. 27, no. 1, pp. 696–711, 2012.
[20]
T. Kidar, M. Thomas, R. Guilbault, and M. El Badaoui, “Comparison between the sensitivity of LMD and EMD algorithms for early detection of gear defects,” Mechanics & Industry, Cambridge University Press, vol. 14, no. 2, pp. 121–127, 2013.
[21]
T. Kidar, M. Thomas, M. El Badaoui, and R. Guilbault, “Application of time descriptors to the modified Hilbert transform of empirical mode decomposition for early detection of gear defects,” in Proceedings of the 2nd International Conference on Condition Monitoring of Machinery in Non-Stationary Operations (CMMNO '12), pp. 471–480, Hammamet, Tunisia, March 2012.
[22]
M. Kedadouche, M. Thomas, and A. Tahan, “Empirical mode decomposition of acoustic emission for early detection of bearing defects,” in Proceedings of the 3rd International Conference on Condition Monitoring of Machinery in Non-Stationary Operations (CMMNO '13), pp. 1–11, Ferrara, Italy, 2013.
[23]
Z. Feng, M. Liang, Y. Zhang, and S. Hou, “Fault diagnosis for wind turbine planetary gearboxes via demodulation analysis based on ensemble empirical mode decomposition and energy separation,” Renewable Energy, vol. 47, pp. 112–126, 2012.
[24]
H. Li, L. Fu, and Z. Li, “Fault detection and diagnosis of gear wear based on Teager-Huang transform,” in Proceedings of the 1st IITA International Joint Conference on Artificial Intelligence (JCAI '09), pp. 663–666, April 2009.
[25]
H. Li, Y. Zhang, and H. Zheng, “Bearing fault detection and diagnosis based on order tracking and Teager-Huang transform,” Journal of Mechanical Science and Technology, vol. 24, no. 3, pp. 811–822, 2010.
[26]
N. Sawalhi, R. B. Randall, and H. Endo, “The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis,” Mechanical Systems and Signal Processing, vol. 21, no. 6, pp. 2616–2633, 2007.
[27]
H. Endo and R. B. Randall, “Enhancement of autoregressive model based gear tooth fault detection technique by the use of minimum entropy deconvolution filter,” Mechanical Systems and Signal Processing, vol. 21, no. 2, pp. 906–919, 2007.
[28]
R. A. Wiggins, “Minimum entropy deconvolution,” Geoexploration, vol. 16, no. 1-2, pp. 21–35, 1978.
[29]
G. L. McDonald, Q. Zhao, and M. J. Zuo, “Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection,” Mechanical Systems and Signal Processing, vol. 21, pp. 2616–2633, 2007.
[30]
Y.-T. Sheen, “A complex filter for vibration signal demodulation in bearing defect diagnosis,” Journal of Sound and Vibration, vol. 276, no. 1-2, pp. 105–119, 2004.
[31]
M. Kedadouche, M. Thomas, and A. Tahan, “Monitoring bearings by acoustic emission: a comparative study with vibration techniques for early detection,” in Proceedings of the 30th Seminar on Machinery Vibration (CMVA '12), pp. 1–17, Niagara Falls, Canada, October 2012.
[32]
M. Thomas, J. Masounave, T. M. Dao, C. T. Le Dinh, and F. Lafleur, “Rolling element bearing degradation and vibration signature relationship,” in Proceedings of the 2nd International Conference on Surveillance Methods and Acoustical and Vibratory Diagnosis, vol. 1, pp. 267–277, SFM, 1995.
