Common maintenance strategies applied to wind turbines include 'Time-Based' which involves carrying out maintenance tasks at predetermined regular-intervals and 'FailureBased' which entails using a wind turbine until it fails. However, the consequence of failure of critical components limits the adequacy of these strategies to support the current commercial drivers of the wind industry. Reliability-Centred Maintenance (RCM) is a technique used mostly to select appropriate maintenance strategies for physical assets. In this paper, a hybrid of an RCM approach and Asset Life-Cycle Analysis technique is applied to Horizontal-Axis Wind Turbines to identify possible failure modes, causes and the resultant effects on system operation. The failure consequences of critical components are evaluated and expressed in financial terms. Suitable Condition-Based Maintenance activities are identified and assessed over the life-cycle of wind turbines to maximise the return on investment in wind farms. 1INTRODUCTION Wind is becoming one of the fastest growing energy sources in many countries seeking to mitigate the effects of global warming and reduce dependency on imported fuels. Very significant financial investments have been made in developing wind farms and the associated grid connection facilities all over the world. Indeed, the wind industry in 2005 spent more than US$14 billion on installing new generating equipment [1]. Progressively, world generated wind energy has now increased to about 59,322 MW [1] from 2,000 MW in 1990 [2] with an annual average growth rate of 26 percent [3]. With this growth has come the need to improve the productivity of wind turbines and to maximise the return on investment in wind farms. Successful future development will require maintenance strategies that are appropriate (technically feasible and economically viable over the lifecycle of wind turbines), given that, "the net revenue from a wind farm is the revenue generated from sale of electricity less operation and maintenance (O&M) expenditure" [4] This paper discusses the current maintenance strategies for wind turbines and identifies the associated problems. The concept and relevance of Reliability Centred Maintenance (RCM) and Asset Life-Cycle Analysis (ALCA) techniques to the wind energy industry are discussed and a Failure Modes and Effects Analysis of a generic horizontal axis wind turbine are presented. A case study is presented to demonstrate the practical application of the hybrid RCM and ALCA to determine suitable Condition Based Maintenance (CBM) activities for a 26x600 kW wind farm. The commercial viability of the CBM activities is assessed using the ALCA technique taking into account geographical location, intermittent operation and value of generation. Non-financial factors are identified and assessed using a Weighted Evaluation (WE) technique. Uncertainties in the financial calculations are risk assessed using a probabilistic technique of the Crystal Ball Monte Carlo simulation. 2RATIONALE AND OBJECTIVE Wind turbines ...

Maintenance optimisation is a crucial issue for industries that utilise physical assets due to its impact on costs, risks and performance. Current quantitative maintenance optimisation techniques include Modelling System Failures MSF (using monte-carlo simulation) and Delay-Time Maintenance Model (DTMM). The MSF investigates equipment failure patterns by using failure distribution, resource availability and spareholdings to determine optimum maintenance requirements. The DTMM approach examines equipment failure patterns by considering failure consequences, inspection costs and the period to determine optimum inspection intervals. This paper discusses the concept, relevance and applicability of the MSF and DTMM techniques to the wind energy industry. Institutional consideration as well as the benefits of practical implementation of the techniques are highlighted and discussed.

Modelling System Failures (MSF) is a unique quantitative maintenance optimisation technique which permits the evaluation of life-data samples and enables the design and simulation of the system's model to determine optimum maintenance activities. In this paper, the approach of MSF is used to assess the failure characteristics of a horizontal axis wind turbine. Field failure data are collated and analysed using the Maximum Likelihood Estimation in the Weibull Distribution; hence shape ( β) and scale (η) parameters are estimated for critical components and subsystems of the wind turbine. Reliability Block Diagrams are designed to model the failures of the wind turbine and of a selected wind farm. The models are simulated to assess the reliability, availability and maintainability of the wind turbine and the farm; taking into account the costs and availability of maintenance crew and spares holding. Optimal maintenance activities are determined to minimise the total life-cycle cost of the wind farm.

The choice of correct inspection intervals poses a serious challenge to industries that utilise physical assets. Too short an interval increases operational cost and waste production time while too long an interval increases the likelihood of unexpected asset failures. FailureModes and Effect Criticality Analysis (FMECA) is a technique that permits qualitative evaluation of assets' functions to predict critical failure modes and the resultant consequences to determine appropriate maintenance tasks for the assets. Delay-Time Maintenance Model (DTMM) is a quantitative maintenance optimisation technique that examines equipment failure patterns by taking into account failure consequences, inspection time and cost in order to determine optimum inspection interval. In this paper, a hybrid of FMECA and DTMM is used to assess the failure characteristics of a selected wind turbine. Optimal inspection intervals for critical subsystems of the wind turbine are determined to minimise its total life-cycle cost.

This is an author produced version of a paper published in Wind Engineering (ISSN 0309-524X, eISSN 2048-402X) This version may not include final proof corrections and does not include published layout or pagination. Citation Details CopyrightItems in 'OpenAIR@RGU', Robert Gordon University Open Access Institutional Repository, are protected by copyright and intellectual property law. If you believe that any material held in 'OpenAIR@RGU' infringes copyright, please contact openair-help@rgu.ac.uk with details. The item will be removed from the repository while the claim is investigated. This is the author's reviewed version, the full published article may be found at http://dx.doi.org/10.1260/0309-524X.37. AbstractOffshore Wind Turbine (OWT) maintenance costs in between 20 -35% of the lifetime power generation cost. Many techniques and tools that are being developed to curtail this cost are challenged by the stochastic climatic conditions of offshore location and the wind energy market. A generic and OWT centric software packages that can smartly adapt to the requirement of any offshore wind farm and optimise its maintenance, logistics and spares-holding while giving due consideration to offshore climate and market conditions will enable OWT operators to centralise their operation and maintenance planning and make significant cost reductions. This work aims to introduce the idea of a comprehensive tool that can meet the above objectives, and give examples of data and functions required. The package uses wind turbine condition monitoring data to anticipate component failure and proposes a time and maintenance implementation strategies that is developed as per the requirements of HSE and government regulations for working in the offshore locations and at heights. The software database contains key failure analysis data that will be an invaluable asset for future researchers, turbine manufacturers and operators, that will optimise OWT power generation cost and better understand OWT working. The work also lists some prevalent tools and techniques developed by industries and researchers for the wind industry.