Operation and maintenance of offshore wind farms will be more difficult and expensive than equivalent onshore wind farms. Accessibility for routine servicing and maintenance will be a concern: there may be times when the offshore wind farm is inaccessible due to sea, wind and visibility conditions. Additionally, maintenance tasks are more expensive than onshore due to distance of the wind farm from shore, site exposure, and the need for specialized lifting equipment to install and change out major components .As a result, the requirement for remote monitoring and condition based maintenance techniques becomes more important to maintain optimum turbine availability levels. The development of a prognostics health management (PHM) capability will allow a strategy that balances risk of running the turbine against lost revenue. Prognostics would give an estimate of the remaining useful life of a component under various loads, thus avoiding component failure. We present a state-space model for predicting the remaining useful life of a component based on vibration signatures. The model dynamics are explained and analysis is performed to evaluate the nature of fault signature distribution, and an indicator of prognostic confidence is proposed. The model is then validated under real world conditions.
How to Cite
extended Kalman filter, prognostics, State Space
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