Wind turbines (WTs) are designed to operate under extreme environmental conditions. This means that extreme and varying loads experienced by WT components need to be accounted for as well as gaining access to wind farms (WFs) at different times of the year. Condition monitoring (CM) is used by WF owners to assess WT health by detecting gearbox failures and planning for operations and maintenance (O&M). However, there are several challenges and limitations with commercially available CM technologies – ranging from the cost of installing monitoring systems to the ability to detect faults accurately. This study seeks to address some of these challenges by developing novel techniques for fault detection using the RMS and Extreme (peak) values of vibration signals. The proposed techniques are based on three models (signal correlation, extreme vibration, and RMS intensity) and have been validated with a time domain data driven approach using CM data of operational WTs. The findings of this study show that monitoring RMS and Extreme values serves as a leading indicator for early detection of faults using Extreme value theory, giving WF owners time to schedule O&M. Furthermore, it also indicates that the prediction accuracy of each CM technique depends on the physics of failure. This suggests that an approach which incorporates the strengths of multiple techniques is needed for holistic health assessment of WT components

The evolution of the wind industry in the last decade has not only seen growth in the installed capacity of turbines and innovation within the industry, but has also seen an increase in research activities in this domain. Gearbox field performance, characterised by reliability, availability and maintainability (RAM), has been a major driver in the research domain due to challenges the industry has faced in gearbox design and operations and maintenance. This paper presents a systematic literature review of the current approaches of performance assessment, such as reliability and maintainability analysis of wind turbine gearboxes with a focus on the use of in-service data. The state-of-the-art in literature are discussed and classified according to key research themes, whilst identifying possible gaps due to lack of literature in specific areas. Also, the future trends in gearbox field performance assessment research are explored. In an attempt to close the gaps in one of the areas not covered in literature, an approach for the estimation of gearbox maintainability was presented. Furthermore, a case study on how preventive maintenance of gearbox bearings which can be applied in practice was carried out to demonstrate the importance of the techniques discussed in this article towards meeting industry׳s needs

Wind turbines are a proven source of clean energy with wind power energy harvesting technologies supplying about 3% of global electricity consumption. However there is an increasing demand on maintenance and operational improvements since turbines have been plagued with downtime problems of major components e.g. gearboxes and generators, especially with offshore turbines which are difficult to access. Reliability Centric Maintenance (RCM) is a way of capturing the potential causes of downtime and poor performance by preventing failures and having a proactive approach to operations and maintenance (O&M). However, for a large fleet of turbines, adopting the RCM approach becomes difficult due to the complexities that arise as a result of the interactions between individual elements that make up the system in the product lifecycle. This paper discusses how a systems thinking approach can be used to identify the relevant aspects and possible interactions between the RCM approach and wind turbine gearboxes and also how the gaps that exist within the system can be closed so as to add value to business. The outcome of the paper is a proposal for applying a systems approach to wind turbine gearbox operation and maintenance, optimising the asset value adding contribution at minimal total cost to the operator

Optimising performance of complex engineering artefacts, which are typically designed to have a useful life of several decades, becomes very difficult during in-service if lessons learnt are not used properly. The authors argue that performance of long-lived complex artefacts can be improved if adequate product in-service data is fed back to the early stages of the product life cycle. This paper discusses an inclusive life cycle approach to optimising product performance by using knowledge and experience gained during in-service. The problem is presented alongside a review of literature of relevant subject areas. A framework for in-service knowledge management is then presented and operationalised through an industrial case study. The framework is developed from the point of view of an integrated product and service provider. The findings from the case study demonstrate how in-service knowledge can be captured, fed back and reused for the design and manufacture stages of the product lifecycle. This enables designers to learn from in-service product performance by informing subsequent designs with in-service knowledge, and consequently improving the through-life product performance