Emerging Challenges and Technologies in Signal Processing for Prognostics and Health Management in Wind Energy



Preston Johnson


The use of condition monitoring (CM) in wind energy machines continues to evolve as wind energy machines grow in size and move offshore. Early and smaller wind generation machines offered little financial incentives for condition monitoring, justifying only simple and inexpensive health monitoring technologies. Today, multi-megawatt wind machines are more complex, more difficult to physically reach, and generate more revenue than previous models. This paper reviews challenges and candidate technologies for next generation condition monitoring in Wind Energy.

Larger wind turbines typically employ Doubly-Fed induction generators with gearbox based drive trains or direct drive generators with multi pole rotors and fixed stators. Both configurations employ variable speed wind driven rotors, variable due to wind speed. Fixed rotor speed signal processing techniques no longer work in a variable speed environment. Synchronous sampling, order analysis, wavelet filters, Cepstrum and related frequency analysis of sensor waveforms are examples of advanced feature extraction tools now available for up-tower condition monitoring systems to address the variable speed nature of modern wind turbines. These signal processing tools operate to reduce and preprocess sensory data producing and extracting signal features. With extracted features, performance prediction and health diagnostics are then able to produce machine degradation rate and degradation levels.

This paper provides a tutorial of signal processing techniques for analysis of sensory information from variable speed rotary machines. The paper concludes with a discussion of prediction and diagnostics techniques which consume the analysis results of previously mentioned signal processing techniques.

How to Cite

Johnson, P. (2010). Emerging Challenges and Technologies in Signal Processing for Prognostics and Health Management in Wind Energy. Annual Conference of the PHM Society, 2(1). https://doi.org/10.36001/phmconf.2010.v2i1.1883
Abstract 76 | PDF Downloads 66



data driven prognostics, signal processing, Wind Turbine, Cepstrum, Wavelet, Order Analysis, Time Synchronous Averaging

Andrew Tan (2010). A Direct Drive to Sustainable Wind Energy, in Wind Systems Magazine www.windsystemsmag.com, page 39, March.

Nanxiong Zhang (2008). Advanced Signal Processing Algorithms and Architectures for Sound and Vibration; National Instruments NI-Week Conference; Presentation TS 1577; Austin, Texas, USA; August.


National Instruments (2009). Sound and Vibration Measurement Suite 2009 Help Manual, June 2009, part number 372416C-01.

National Instruments (2009). National Instruments LabVIEW Advanced Signal Processing Toolkit Help Manual, June 2009, part number 372656A-01.

E. Bechhoefer and M. Kingsley (2009). A Review of Time Synchronous Averaging Algorithms. Annual Conference of the Prognostics and Health Management Society.
P. McFadden and M. Toozhy (2000). Application of Synchronous Averaging to Vibration Monitoring of Roller Element Bearings; Mechanical Systems and Signal Processing, 14(6), 891-906.

National Instruments (2010). Fatigue Analysis in LabVIEW, May 2010, Developer Zone Tutorial 10991, www.ni.com/zone.

Dr. Jay. Lee (2009) Advanced Prognostics for Smart Systems. Intelligent Maintenance Systems (IMS) Introduction, pp. 6-8.

IMS Center (2009). IMS Teams Win 2009 PHM Data Challenge Competition, news release.

Intelligent Maintenance Systems (2007). Watchdog Agent TM Documentation, Center for Intelligent Maintenance Systems, May 9.
Technical Research Papers