Fault Diagnosis Methods for Wind Turbines Health Monitoring: a Review



Bouthaina Abichou Diana Flórez Moamar Sayed-Mouchaweh Houari Toubakh Bruno François Nicolas Girard


Recently, the rapid expansion of wind energy activity has led to an increasing number of publications that deal with wind turbine health monitoring. In real practice, implementing a prognostics and health management (PHM) strategy for wind turbines is challenging. Indeed, wind turbines are complex electro-mechanical systems that often work under rapidly changing environment and operating load conditions. Although several review papers that address wind turbines fault diagnosis were published, they are mostly focused on a specific component or on a specific category of methods. Therefore, a larger snapshot on recent advances in wind turbine fault diagnosis is presented in this paper. Fault diagnosis approaches could be grouped in three major categories according to the available a priori knowledge about the system behavior: quantitative/qualitative model, signal analysis and artificial intelligence based approaches. Each of the proposed methods in the literature has its advantages and drawbacks. Therefore, a comparison between these methods according to some meaningful evaluation criteria is conducted.

How to Cite

Abichou, B., Flórez, D., Sayed-Mouchaweh, M., Toubakh, H., François, B., & Girard, N. (2014). Fault Diagnosis Methods for Wind Turbines Health Monitoring: a Review. PHM Society European Conference, 2(1). https://doi.org/10.36001/phme.2014.v2i1.1492
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fault diagnosis, Wind turbines, Pattern recognition

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