Intelligent Health Management for Micro-Scale Wind Turbine Bearings

Abufroukh, Ahmed N-s (2018) Intelligent Health Management for Micro-Scale Wind Turbine Bearings. Masters thesis, University of Central Lancashire.

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The noticeable expansion of the wind turbine industry in the UK has been encouraged by the government, which announced a range of incentives to meet the increased demand for wind turbine technologies, as a contribution to the effects against climate change. The UK is embracing the challenge of preventing 0.8 million tonnes of pollution through deploying approximately 806MW of small and medium turbines by 2023. Consequently, appropriate maintenance strategies to lengthen the life of turbine elements, including main shaft, bearings, gearbox and generator should be considered. Bearings tend to be one of the most common defective elements. Defects may occur for many reasons including contamination, lubrication problems and excessive operational conditions; thus, failure can take several forms and could arise in any part of the bearing elements. These factors are not included in the basic rating life (L10) – calculation proposed by ISO281. Thus determining the life of the bearing is challenging.
A thorough literature review was conducted to investigate the research field including, common failures that occur in micro-scale wind turbine elements such as: bearings, generator, brake and main shaft. This was followed by mutual condition monitoring techniques that are currently used to detect failures. Lastly, intelligent methods that can be incorporated to enhance the performance of the machinery were highlighted.
Illustrations of hardware and software developments were covered in the next chapter, including all the equipment used to construct the test bench throughout this research and all the sensors and controllers that were interfaced to develop the IHMS.
The system then underwent preliminary verification, and finally several tests including bearing system failure and bearing life, were conducted to be studied graphically. Conclusions were drawn from this work, and future directions considered.
This thesis presents a prototype of an Intelligent Health Management System (IHMS) for micro-scale wind turbine bearings. This development addresses various issues in determining bearing life, detecting failure in the machinery. It proposes the use of an intelligent analysis approach that integrates information from key sensors to monitor bearing life. The IHMS was evaluated using a test apparatus developed by the author.

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