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Durham University

Department of Engineering

Staff Profile

Publication details for Dr Christopher Crabtree

Zappalá, D., Tavner, P.J. , Crabtree, C.J. & Sheng, S. (2014). Side-band algorithm for automatic wind turbine gearbox fault detection and diagnosis. IET Renewable Power Generation 8(4): 380-389.

Author(s) from Durham


Improving the availability of wind turbines is critical for minimising the cost of wind energy, especially offshore. The development of reliable and cost-effective gearbox condition monitoring systems (CMSs) is of concern to the wind industry, because the gearbox downtime has a significant effect on the wind turbine availabilities. Timely detection and diagnosis of developing gear defects is essential for minimising an unplanned downtime. One of the main limitations of most current CMSs is the time consuming and costly manual handling of large amounts of monitoring data, therefore automated algorithms would be welcome. This study presents a fault detection algorithm for incorporation into a commercial CMS for automatic gear fault detection and diagnosis. Based on the experimental evidence from the Durham Condition Monitoring Test Rig, a gear condition indicator was proposed to evaluate the gear damage during non-stationary load and speed operating conditions. The performance of the proposed technique was then successfully tested on signals from a full-size wind turbine gearbox that had sustained gear damage, and had been studied in a National Renewable Energy Laboratory's (NREL) programme. The results show that the proposed technique proves efficient and reliable for detecting gear damage. Once implemented into the wind turbine CMSs, this algorithm can automate the data interpretation, thus reducing the quantity of the information that the wind turbine operators must handle.