A novel architecture and system for the provision of Reliability Centred Maintenance (RCM) for offshore wind power generation is presented. The architecture was developed by conducting a bottom-up analysis of the data required to support RCM within this specific industry, combined with a top-down analysis of the required maintenance functionality. The architecture and system consists of three integrated modules for intelligent condition monitoring, reliability and maintenance modelling, and maintenance scheduling that provide a scalable solution for performing dynamic, efficient and cost-effective preventative maintenance management within this extremely demanding renewable energy generation sector. The system demonstrates for the first time the integration of state-of-the-art advanced mathematical techniques: Random Forests, dynamic Bayesian networks and memetic algorithms in the development of an intelligent autonomous solution. The results from the application of the intelligent integrated system illustrated the automated detection of faults within a wind farm consisting of over 100 turbines, the modelling and updating of the turbines' survivability and creation of a hierarchy of maintenance actions, and the optimizing of the maintenance schedule with a view to maximizing the availability and revenue generation of the turbines.

Prognostics of transformer remaining life can be achieved through a statistical technique called particle filtering, which gives a more accurate prediction than standard methods by quantifying sources of uncertainty

The reliability of a multi-state system is considered. The system is subject to both internal wear-out and external shocks causing damage that cumulates as shocks follow one another. As a consequence of this cumulating damage, the system wear-out process can be affected. The study of the system is achieved by means of phase-type distributions, which are used to model: the inter-arrival times between shocks, the magnitude of the damage due to the shocks, and the lifetime distribution of the system between shocks. In the latter case, the phases of the phase-type distribution refer to the different degradation levels (‘states’) of the system. The lifetime distribution of the system is affected by the shocks in different ways: if the cumulated damage after a shock exceeds q predefined thresholds, the system can no longer evolve in the first q least degraded levels; the corresponding phase-type distribution is then defined on the remaining phases. But after each shock causing no threshold to be exceeded, only the initial probability vector of the phase-type distribution is modified in order to account for a decrease in the expected residual lifetime of the system. Two particular cases are studied. First, several thresholds on the cumulated damage can be exceeded following the occurrence of one shock; secondly, only one threshold at a time can be exceeded

Abstract-Prognostics predictions estimate the remaining useful life of assets. This information enables the implementation of condition-based maintenance strategies by scheduling intervention when failure is imminent. Circuit breakers are key assets for the correct operation of the power network, fulfilling both a protection and a network reconfiguration role. Certain breakers will perform switching on a deterministic schedule, while operating stochastically in response to network faults. Both types of operation increase wear on the main contact, with high fault currents leading to more rapid ageing. This paper presents a hybrid approach for prognostics of circuit breakers, which integrates deterministic and stochastic operation through Piecewise Deterministic Markov Processes. The main contributions of this paper are (i) the integration of hybrid prognostics models with dynamic reliability concepts for a more accurate remaining useful life forecasting and (ii) the uncertain failure threshold modelling to integrate and propagate uncertain failure evaluation levels in the prognostics estimation process. Results show the effect of dynamic operation conditions on prognostics predictions and confirm the potential for its use within a condition-based maintenance strategy.

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