Abstract
Condition monitoring (CM) systems are installed in wind turbines (WTs) in order to avoid component downtime and reduce maintenance costs. Vibration monitoring is widely used for the WT gearbox, which is a component with a significant downtime. Given that the installed wind capacity grows, the volume of CM data increases, making manual interpretation of vibration signals challenging. Therefore, there is a need for an efficient and automated maintenance decision support system. The aim to this paper is to propose an automated framework for gearbox incipient failure diagnosis. The framework utilises vibration signals and performs health estimation and fault isolation based on signal processing and artificial intelligence (AI) techniques. The methodology is demonstrated through a case study of vibration data from operating WTs with known gearbox failures. The study can be used to optimise wind turbine maintenance actions.
Highlights
Operation and maintenance (O&M) costs are a major contributor to the total cost of energy of large wind farms
In order to reduce those costs, condition monitoring (CM) systems are installed in wind turbines
The promising results suggest that artificial intelligence (AI) can be used in order to make more informed maintenance decisions and avoid manual interpretation of a large amount of wind turbine CM data
Summary
Operation and maintenance (O&M) costs are a major contributor to the total cost of energy of large wind farms. Wind turbine (WT) failures and unscheduled maintenance actions increase the O&M costs and downtime, compromising the annual energy production. In order to reduce those costs, condition monitoring (CM) systems are installed in wind turbines. The WT gearbox is one of the components with the highest downtime [1]. Vibration based CM has proven to be effective in this component. Accelerometers installed on the gearbox surface transmit high frequency signals that can give useful information about the health state of the gearbox
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
