Wind Turbine Bearing Fault Detection Using Adaptive Resampling and Order Tracking



Published Nov 20, 2020
Cody Walker Jamie Coble


Wind energy is growing increasingly popular in the United States, so it is imperative to make it as cost competitive as possible. Operations and Maintenance (O&M) make up 20-25% of the total cost of onshore wind projects. Unplanned maintenance contributes approximately 75% of the total maintenance costs (WWEA, 2012). Condition-based maintenance strategies intend to maximize the uptime by reducing to the amounts of unplanned maintenance. This should result in an overall decrease in the cost of maintenance. Wind turbines produce an interesting challenge, because their main shaft rotation is both slow and nonstationary. Through the use of adaptive resampling and order tracking, both of these challenges were combated as the bearing fault was identified in the order spectrum then tracked as it progressed. The fault was identified as an outer race defect on the main bearing that initiated sometime during or before installation. The total energy in the order spectrum around the bearing fault rate was identified as a potential front-runner for a prognostic parameter. This paper presents a case study application to operational wind turbine bearing data to demonstrate the ease and intuitiveness of combining adaptive resampling and order tracking to diagnose faults for slow, nonstationary bearings. Prognosis of remaining useful life is proposed with features extracted from the order spectrum, but additional data are needed to develop and demonstrate this analysis.

Abstract 276 | PDF Downloads 303



fault detection, vibration analysis, Wind Turbines

Bechhoefer, E., Hecke, B. V.,& He, D. (2013). Processing for improved spectral analysis [Journal Article]. phmsociety, 4. Retrieved from
Bechhoefer, E., & Kingsley, M. (2009). A review of time synchronous average algorithms [Conference Paper]. San Diego, CA: Annual conference of the Prognostics and Health Management society.
Blough, J. R. (2006). Adaptive resampling - transforming from the time to the angle domain [Journal Article]. Proceedings of International Modal Analysis Conference( 24), 315-329.
Coble, J., & Hines, J. W. (2012). Identifying suitable degradation parameters for individual-based prognostics [Book Section]. In S. Kadry (Ed.), Prognostics of engineering systems: Methods and techniques (p. 135-150).
Colombo, J., Arora, R., Depace, N. L., & Vinik, A. I. (2015). Clinical autonomic dysfunction: Measurement, indications, therapies, and outcomes [Book]. Switzerland: Springer.
Dolan, D. S. L., & Lehn, P. W. (2006, Sept). Simulation model of wind turbine 3p torque oscillations due to wind shear and tower shadow. IEEE Transactions on Energy Conversion, 21(3), 717-724. doi: 10.1109/TEC.2006.874211
Feldman, M. (2011). Hilbert transform applications in mechanical vibration. John Wiley and Sons, Ltd.
Holtz, J., & Springob, L. (1996, Apr). Identification and compensation of torque ripple in high-precision permanent magnet motor drives. IEEE Transactions on Industrial Electronics, 43(2), 309-320. doi: 10.1109/41.491355
Huang, N., & Shen, S. (2014). Hilbert-huang transform and its applications 2nd edition [Book]. Singapore: World Scientific Publishing Co.
Hussain, S., & Gabbar, H. A. (2013). Vibration analysis and time series prediction for wind turbine gearbox prognostics. IJPHM Special Issue on Wind Turbine PHM (Color), 69.
Jardine, A. K. S., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance [Journal Article]. Mechanical Systems and Signal Processing, 20, 1483-1510.
Li, R., & Frogley, M. (2013). On-line fault detection in wind turbine transmission system using adaptive filter and robust statistical features. IJPHM Special Issue on Wind Turbine PHM (Color), 115.
Niknam, S. A., Thomas, T., Hines, J. W., & Sawhney, R. (2013). Analysis of acoustic emission data for bearings subject to unbalance. International Journal of Prognostics and Health Management, 4, 80–89.
Rai, V. K., & Mohanty, A. R. (2006). Bearing fault diagnosis using fft of intrinsic mode functions in hilberthuang transform [Journal Article]. ScienceDirect, Mechani-cal Systems and Signal Processing(21), 2607-2615.
Tandon, N., & Choudhury, A. (1999). A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings [Journal Article]. Tribology International, 32(8), 469-480. Retrieved from doi:
Tolstov, G. P. (1962). Fourier series, translated by richard a. silverman [Book]. New York: Dover Publications.
White, J. R., Adams, D. E., & Rumsey, M. A. (2009). Operational load estimation of a smart wind turbine rotor blade. In Health monitoring of structural and biological systems 2009 (Vol. 7295, p. 72952D).
Williams, T., Ribadeneira, X., Billington, S., & Kurfess, T. (2001). Rolling element bearing diagnostics in run-tofailure lifetime testing [Journal Article]. Mechanical Systems and Signal Processing, 15, 979-993.
WWEA. (2012). Quarterly bulletin. World Wind Energy Association Bulletin, 3, 30-36.
Zhang, H.-G., Zhang, S., & Yin, Y.-X. (2014). A novel improved elm algorithm for a real industrial application [Journal Article]. Mathematical Problems in Engineering, 2014, 1-7. doi: 10.1155/2014/824765
Zhu, J., Yoon, J. M., He, D., Qu, Y., & Bechhoefer, E. (2013). Lubrication oil condition monitoring and remaining useful life prediction with particle filtering. International Journal of Prognostics and Health Management, 4, 124–138.
Zipp, K. (2012). Understanding cost for large wind-turbine drivetrains [Web Page]. Retrieved from
Technical Papers