Vibration-based bearing fault detection on experimental wind turbine gearbox data



Cédric Peeters Patrick Guillaume Jan Helsen


Rolling element bearing faults are one of the most common defects in rotating machinery. The detection of these faults has lately attracted an increasing amount of attention in the industry. Detecting the faults in their incipient phase can prevent a more catastrophic breakdown of a machine and can save a company time and money. An often occurring problem in analyzing vibration measurements of a rotating machine is the presence of harmonic signal components originating from different machine parts. This paper focuses on separating the bearing fault signals from other masking signal content coming from elements like gears or rotating shafts. The separation is based on the assumption that signal components of gears or shafts are deterministic and appear as clear peaks in the frequency spectrum, whereas bearing signals are stochastic due to random jitter on their fundamental period and can be classified as cyclostationary. A technique that has recently gained more attention for separating these two types of signals is the cepstral editing procedure (CEP). This preprocessing method is investigated further in this paper as an automated procedure. The cepstral editing procedure makes use of the fact that deterministic components appear in the cepstrum as narrow peaks and removal of these cepstral peaks reduces the amplitude of the corresponding discrete components. The residual stochastic signal after cepstral editing should consist mainly out of bearing fault signals in certain frequency bands. To analyze the residual signal the squared envelope spectrum is examined. The issue of selecting the proper frequency band where to demodulate the signal necessary for enveloping is addressed through the use of spectral kurtosis. The performance of the developed methods will be validated on experimental data from the National Renewable Energy Laboratory (NREL). This data has been made available by NREL in the context of the wind turbine gearbox condition monitoring round robin study. As such other research teams from around the world have analyzed this data as well and published their findings. The measurements were done on a wind turbine gearbox in a healthy and damaged condition and the setup is documented extensively, making it well suited for
a performance comparison. A summary of the findings on the NREL data is discussed in this paper.

How to Cite

Peeters, C., Guillaume, P., & Helsen, J. (2016). Vibration-based bearing fault detection on experimental wind turbine gearbox data. PHM Society European Conference, 3(1).
Abstract 120 | PDF Downloads 75



data preprocessing, bearing fault detection, Condition Based Monitoring

A.I.Technologies, A. I. (2009). Lubricant failure = bearing failure. Machinery Lubrication Magazine(1).
Antoni, J. (2007). Fast computation of the kurtogram for the detection of transient faults. Mechanical Systems and Signal Processing, 21(1), 108–124.
Antoni, J., Daniere, J., & Guillet, F. (2000). Blind identification of nonminimum phase systems using the mean differential cepstrum. In Signal processing conference, 2000 10th european (pp. 1–4).
Antoni, J., & Randall, R. (2003). A stochastic model for simulation and diagnostics of rolling element bearings with localized faults. Journal of vibration and acoustics, 125(3), 282–289.
Antoni, J., & Randall, R. (2004a). Unsupervised noise cancellation for vibration signals: part i evaluation of adaptive algorithms. Mechanical Systems and Signal Processing, 18(1), 89–101.
Antoni, J., & Randall, R. (2004b). Unsupervised noise cancellation for vibration signals: part ii a novel frequency domain algorithm. Mechanical Systems and Signal Processing, 18(1), 103–117.
Bogert, B. P., Healy, M. J., & Tukey, J. W. (1963). The quefrency alanysis of time series for echoes: Cepstrum, pseudo-autocovariance, cross-cepstrum and saphe cracking. In Proceedings of the symposium on time series analysis (Vol. 15, pp. 209–243).
Boll, S. F. (1979). Suppression of acoustic noise in speech using spectral subtraction. Acoustics, Speech and Signal Processing, IEEE Transactions on, 27(2), 113–120.
Borghesani, P., Pennacchi, P., Randall, R., Sawalhi, N., & Ricci, R. (2013). Application of cepstrum prewhitening for the diagnosis of bearing faults under variable speed conditions. Mechanical Systems and Signal Processing, 36(2), 370–384.
Childers, D. G., Skinner, D. P., & Kemerait, R. C. (1977). The cepstrum: A guide to processing. Proceedings of the IEEE, 65(10), 1428–1443.
Cohen, R. (2012). Signal denoising using wavelets. Project Report, Department of Electrical Engineering Technion, Israel Institute of Technology,
Haifa. Courrech, J., & Gaudet, M. (1998). Envelope analysis-the key to rolling-element bearing diagnosis. Br¨uel & Kjær Application Notes.
Dalpiaz, G., Rubini, R., D’Elia, G., Cocconcelli, M., Chaari, F., Zimroz, R., . . . Haddar, M. (2013). Advances in condition monitoring of machinery in non-stationary operations. In Proceedings of the third international conference on condition monitoring of machinery in non-stationary operations cmmno.
Donoho, D. L., & Johnstone, J. M. (1994). Ideal spatial adaptation by wavelet shrinkage. Biometrika, 81(3), 425–455.
Graney, B. P., & Starry, K. (2012). Rolling element bearing analysis. Materials Evaluation, 70(1), 78.
Ho, D., & Randall, R. (2000). Optimisation of bearing diagnostic techniques using simulated and actual bearing fault signals. Mechanical systems and signal processing, 14(5), 763–788.
Kamath, S., & Loizou, P. (2002). A multi-band spectral subtraction method for enhancing speech corrupted by colored noise. In Ieee international conference on acoustics speech and signal processing (Vol. 4, pp. 4164–4164).
Kilundu, B., Ompusunggu, A. P., Elasha, F., & Mba, D. (2014). Effect of parameters setting on performance of discrete component removal (dcr) methods for bearing faults detection. In Proceedings of the european conference of the prognostics and health management (phm) society, nantes (france), 8th-10th july.
Lebart, K., Boucher, J.-M., & Denbigh, P. (2001). A new method based on spectral subtraction for speech dereverberation. Acta Acustica united with Acustica, 87(3), 359–366.
Martin, R. (1994). Spectral subtraction based on minimum statistics. power, 6, 8.
McFadden, P., & Smith, J. (1985). The vibration produced by multiple point defects in a rolling element bearing. Journal of sound and vibration, 98(2), 263–273.
McFadden, P., & Toozhy, M. (2000). Application of synchronous averaging to vibration monitoring of rolling element bearings. Mechanical Systems and Signal Processing, 14(6), 891–906.
Ompusunggu, A. P. (n.d.). Automated cepstral editing procedure (acep) as a signal pre-processing in vibrationbased bearing fault diagnostics.
Pasti, L., Walczak, B., Massart, D., & Reschiglian, P. (1999). Optimization of signal denoising in discrete wavelet transform. Chemometrics and intelligent laboratory systems, 48(1), 21–34.
Polydoros, A., & Fam, A. T. (1981). The differential cepstrum: definition and properties. In Proc. ieee int. symp. circuits syst (pp. 77–80).
Randall, R., & Hee, J. (1982). Cepstrum analysis. Wireless World, 88, 77–80.
Randall, R., Sawalhi, N., & Coats, M. (2011). A comparison of methods for separation of deterministic and random signals. International Journal of Condition Monitoring, 1(1), 11–19.
Randall, R. B. (2011). Vibration-based condition monitoring: industrial, aerospace and automotive applications. John Wiley & Sons.
Randall, R. B. (2013). A history of cepstrum analysis and its application to mechanical problems. In International conference at institute of technology of chartres, france (pp. 11–16).
Randall, R. B., Antoni, J., & Chobsaard, S. (2001). The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals. Mechanical systems and signal processing, 15(5), 945–962.
Randall, R. B., & Sawalhi, N. (2011). A new method for separating discrete components from a signal. Sound and Vibration, 45(5), 6.
Randall, R. B., & Sawalhi, N. (2014). Cepstral removal of periodic spectral components from time signals. In Advances in condition monitoring of machinery in nonstationary operations (pp. 313–324). Springer.
Sawalhi, N., & Randall, R. (2011). Signal pre-whitening using cepstrum editing (liftering) to enhance fault detection in rolling element bearings. In Proceedings of the 24 international congress on condition monitoring and diagnostic engineering management (comadem2011), may (pp. 330–336).
Sawalhi, N., & Randall, R. B. (2004). The application of spectral kurtosis to bearing diagnostics. In Proceedings of acoustics (pp. 3–5).
Sheldon, J., Mott, G., Lee, H., & Watson, M. (2014). Robust wind turbine gearbox fault detection. Wind Energy, 17(5), 745–755.
Sheng, S. (2012). Wind turbine gearbox condition monitoring round robin study–vibration analysis. Contract, 303, 275–3000.
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