PhD Studentship: Bearing Sensing to Improve Wind Turbine Gearbox Reliability

PhD Studentship: Bearing Sensing to Improve Wind Turbine Gearbox Reliability

nCATS Group

Location:  Highfield Campus
Closing Date:   Thursday 06 July 2017
Reference:  754316BX

Project Reference: EngSci-nCATS-121

Project Theme:  Materials & Surface Engineering, Mechatronics, Signal Processing & Control

Wind energy has been the fastest growing renewable energy source in the world since 1990, which has resulted in a speedy development of wind turbine design and manufacture in recent years. The increase in the size of wind turbines (blade size from 15 m for a 30 kW wind turbine to over 120 m for a 7 MW one that is under development) has resulted in larger drive train systems, which unfortunately are accompanied by the escalation of tribological issues, such as increasing loads on gearbox bearings; lowering hub rotation speed to limit tip speeds which induces lubrication issues in bearings and gears under high loads and slow rotating speeds; increasing of friction torque in large rolling element bearings. About 80% of horizontal axis wind turbines use geared drive train systems to transmit the input speeds (typically 10-15 rpm) to output speeds (typically 1200-1800 rpm), where a large number of unexpected failures are experienced in gearbox bearings due to a lack of understanding the mechanical systems and their operational conditions. Although wind turbines are designed following IEC guidelines, ISO Standards and certification bodies requirements, to be operated for a minimum life of 20 years, where the gears should have sufficient durability and tooth bending strength and the rolling element bearings should have L10>175,000 hours (estimated bearing fatigue life). However, the actual service life of wind turbine gearboxes is found to be an average of about 5 years due to premature failures. It is unspeakably short compared with that of conventional steam turbines, which can exceed 50 years. Out of all the critical components in a wind turbine system, gearbox failure has been found to cause the highest down time (about 350 hours per failure) with an estimated cost of £300k per replacement for onshore and over £1m for offshore turbines. To reduce the cost of wind energy and prevent wind turbine catastrophic failures, it is essential to have a robust sensing system that can monitoring the health of wind turbines thus detect and predict failures at premature stages.

Currently, the operation and maintenance (O&M) in wind industry occupies 10-15% and 20-25% of the overall cost for onshore and off-shore wind turbines respectively, where unscheduled maintenance accounts for 30-60% of the total expenditure. With the high expectations for offshore wind to meet renewable energy targets; where service and maintenance access can be extremely difficult, improving reliability and availability of the wind turbines becomes even more important. Current wind turbine condition monitoring systems based on vibration, debris analysis and temperature monitoring techniques are only useful in detecting defects and faults that have become relatively large at the very end of machine’s service life but are unable to predict incipient faults and provide early warnings. This PhD project aims to develop a robust sensing system based on multiple sensors, wireless techniques and advanced data processing methods to predict and prevent premature wind turbine gearbox bearing failures.

If you wish to discuss any details of the project informally, please contact Dr Ling Wang, nCATS research group, Email: ling.wang@soton.ac.uk, Tel: +44 (0) 2380 59 5076.

To apply please use the following link http://www.southampton.ac.uk/engineering/postgraduate/research_degrees/apply.page? and select Faculty of Engineering and the Environment.

Further details:

• Job Description and Person Specification