Assets reliability is a key issue to consider in the maintenance management policy and given its importance several estimation methods and models have been proposed within the reliability engineering discipline. However, these models involve certain assumptions which are the source of different uncertainties inherent to the estimations. An important source of uncertainty is the operational context in which the assets operate and how it affects the different failures. Therefore, this paper contributes to the reduction of the uncertainty coming from the operational context with the proposal of a novel method and its validation through a case study. The proposed model specifically addresses changes in the operational context by implementing dynamic capabilities in a new conception of the Proportional Hazards Model. It also allows to model interactions among working environment variables as well as hidden phenomena thanks to the integration within the model of artificial neural network methods.

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Energies 2019, 12, 2036 2 of 17 account for up to one quarter of the proportion of the LCoE, with 80% of this expenses directly attributed to maintenance [10][11][12].In view of the maintenance role in the LCoE and thus in the profitability of WFs, it is important to consider models for optimization of O&M plans and decisions [2]. The evolution of maintenance models and methodologies have kept pace with the constant technological evolution of Wind Turbines (WTs) [3]. According to [3], the goal of all the approaches and methodologies is determining the most adequate maintenance plan, the management of the resources, and the aspects related to Reliability, Availability, and Maintainability (RAM) of the WTs.Within such context, the WF operators are bound to develop new techniques and decision-support tools for optimal maintenance strategies, if they strive to maximize the profitability of the investment [8]. Accordingly, maintenance management discipline acquires a highly significant position since it provides a comprehensive perspective for the management of WTs, allowing for optimal maintenance strategies which reduce maintenance costs while maximizing availability [3,5].The decision-making process in the asset management field has been divided into strategic (long-term), tactic (medium-term), and operational (short-term) to achieve excellence in maintenance [13][14][15]. It is the purpose of the research in this paper to address the different aspects of the maintenance decision-making process. This is done by answering the research question of whether it is possible to achieve maintenance excellence for the lifecycle of WFs by a maintenance strategy which considers the different behaviors of the WTs and integrated business-related objectives.Aiming at providing an answer for the aforementioned research question, a technical framework for managing maintenance is proposed in the paper. The framework is a comprehensive proposal that considers different aspects regarding the maintenance of a WF. Within the possibilities offered by the current trend for big data, certain key aspects to create a failure database are integrated in the framework, which enables a clustering approach based on the failure behaviors of the different failure modes of each WT. This approach supports an opportunistic maintenance policy with dynamic thresholds that regard not only to the reliability of the assets but also business considerations. Besides, the framework also incorporates the strategic view through a Life-Cycle Cost (LCC) perspective integrated by means of a multi-objective optimization and supported by simulation techniques that provide valuable information to find an attractive trade-off between cost and performance.

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