Nowadays offshore wind energy is the renewable energy source with the highest growth. Offshore wind farms are composed of large and complex wind turbines, requiring a high level of reliability, availability, maintainability and safety (RAMS). Firms are employing robust remote condition monitoring systems in order to improve RAMS, considering the difficulty to access the wind farm. The main objective of this research work is to optimise the maintenance management of wind farms through the fault probability of each wind turbine. The probability has been calculated by Fault Tree Analysis (FTA) employing the Binary Decision Diagram (BDD) in order to reduce the computational cost. The fault tree presented in this paper has been designed and validated based on qualitative data from the literature and expert from important European collaborative research projects. The basic events of the fault tree have been prioritized employing the criticality method in order to use resources efficiently. Exogenous variables, e.g., weather conditions, have been also considered in this research work. The results provided by the dynamic probability of failure and the importance measures have been employed to develop a scheduled maintenance that contributes to improve the decision making and, consequently, to reduce the maintenance costs.

Electrical and electronic components are very important subcomponents in modern industrial wind turbines. Complex multimegawatt wind turbines are continuously being installed both onshore and offshore, continuously increasing the demand for sophisticated electronic and electrical components. In this work, most critical electrical and electronic components in industrial wind turbines have been identified and the applicability of appropriate condition monitoring processes simulated. A fault tree dynamic analysis has been carried out by binary decision diagrams to obtain the system failure probability over time and using different time increments to evaluate the system. This analysis allows critical electrical and electronic components of the converters to be identified in different conditions. The results can be used to develop a scheduled maintenance that improves the decision making and reduces the maintenance costs.

This paper presents two methods for supporting investments and resource allocation in a constrained risky environment. These methods are based on the application of logical decision trees and binary decision diagrams as an approach that allows quantitative analysis of a qualitative study. The scenario considered in this paper is a decision-making process under risk environment, where stochastic variables are considered. The two novel procedures are introduced to facilitate the resource allocation as the objective of the decision-making process. The first procedure uses the analytic expression provided by binary decision diagrams as an objective function of a non-linear programing model. The second procedure introduces an importance measure that takes into account some external constraints, unlike the classical importance measures that only consider the topology of the tree. The first technique will optimise the outcomes and the second will provide a good approximation of the outcomes using simpler calculations.

Firms require to optimize any decisions taken for increasing their competitiveness. Each decision will depend of a large number of causes. The relations between the causes and the decisions are effected by the decision making process. An approach to finding the best solution to each logistical or organizational problem is to identify the main causes which are involved for the best decision. A qualitative and a quantitative analysis to facilitate the decision making process is carried out in this paper employing logical decision trees together with binary decision diagrams. This paper presents a novel comparison between different ranking methods applied to several case studies of logical decision trees. The ranking methods considered in this paper are ‘Level’, ‘AND’ and the topological heuristic methods Top-Down-Left-Right, Depth-First-Search and Breath-First-Search. Importance measures have been used for ranking the basic events of the logical decision trees according to their contribution to the top event in order to establish the main causes and, therefore, to determine the critical causes. The importance measures methods employed in this paper are: Fussell–Vesely, Birnbaum and criticality. Finally, an analysis of these importance measures is performed that is based on a comparative study of the different methods for setting the importance measures in order to determine the differences between them.