Presentations

Abstract

Wind turbine blade failures are prevalent in the industry, and failure rates remain high. Blades can fail in many ways, some of which are more severe than others. Broadly speaking, failure modes can be sorted into two categories: (1) External failures and failures near the tip. For example, leading edge erosion, surface cracks and tip separation. Generally speaking, these types of failure often develop slowly over a long period of time and are unlikely to lead to the loss of a blade, except in the most extreme cases. (2) Internal failures and failures near the root. For example, internal cracks in the main spar or shear web, and separation of the blade from the pitch bearing. These failures can occur relatively quickly - in some cases developing over just a few weeks- and can be catastrophic for the blade or turbine. Because the nature, severity and timescale of these failures modes varies, the appropriate monitoring approach also varies. For category (1) external failures, it is common to employ annual drone inspections or ground-based visual inspections. However, drone inspections are not as effective at enabling predictive maintenance for category (2) internal failures. For example, a crack in the main spar or shear web will only become visible on the outside of the blade at a very late stage - by which time the crack may be over a metre in length, with the only solution being to stop the turbine immediately and replace the blade. Consequently, better detection methods are required for internal failure modes in order to enable predictive maintenance and repair of the blade, before failures become catastrophic. This paper reports on research into methods for detecting internal blade failures, and proposes a future solution for blade health monitoring which combines drone inspections with internal sensing.