Subventions et des contributions :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023)

Design and operation of wind power stations are guided by the “L 3 conditions”, namely, low cost, long-lasting, and low service requirement. Not only do wind turbine components fail at higher rates because of their extreme operating conditions, the costs associated with the failure are also higher than other machinery systems due to the need for special equipment such as a large crane. Furthermore, wind turbines may not be accessible at times of extreme weather conditions, causing extended shutdown periods and loss of production. A reliable and automated machine health condition monitoring system can enable effective predictive maintenance practice and contribute directly to all three L’s. Such a system should have the ability to detect faults, link them to root causes, and predict machine health state at future times.

In the general area of machine condition monitoring, solutions in fault diagnosis are becoming increasingly sophisticated with improved reliability in fault classification and robustness to operating and environmental condition variations. However, very few solutions exist that can determine root causes of faults or predict failure at the system level. These are the main reasons for the slow industry adoption of research and development results because mere detection of faults without actionable information, such as when and where failure will occur, does not necessarily translate to cost savings.

A system level model that includes component models based on underlying physical principles can provide the key to a solution with the much needed fault prediction capabilities. In this research program, we aim to establish an integrated wind turbine system model that can represent a digital copy of the actual system particularly in terms of its health condition. At any point of time, this model can indicate fault location and severity. It can be used to simulate the system’s behavior as the result of fault progression in future times to enable failure prediction. It can also be used to analyze the effect of a localized fault on system’s overall dynamics behavior and the well-being of other components to inform effective intervention. The successful delivery of the research outcome will represent a leap forward toward building an effective predictive maintenance solution for the wind energy and other industries.