Reliability and Preventive Maintenance

Abstract

In this chapter, the authors explain how to perform the core analysis of a Reliability Centered Maintenance (RCM) process for an offshore wind turbine. The aim is to provide an engineering guide which can improve the maintenance of the system, and consequently increases its availability and the production of energy. The initial investigations have been carried out using a database for an onshore 5 MW wind turbine; the data has then been converted using a proper conversion factor, so that it can be used for a 10 MW offshore turbine case. The reliability and availability of the entire offshore wind turbine have been calculated through Reliability Prediction and a Reliability Block Diagram (RBD). In addition, a failure mode analysis is done using FMECA, in order to identify the most important failure modes in a risk priority order, and to the note the effect of propagation of each functional failure. The maintenance part of the RCM analysis has also been studied, to facilitate the creation of an optimum packaging of preventive maintenance tasks, which can help to avoid the functional failures of items throughout the system. Although the main target of the RCM is to reduce the downtime of the wind turbine, a reduction in Life Cycle Costs can be also accomplished through this process.

Automated wind turbine maintenance scheduling

“…Long term maintenance policies must cover both of these. Corrective maintenance is normally carried out once a fault has been detected, whereas, preventive maintenance is generally performed according to calendar-based pre-determined intervals such as biannual, annual, biennial and quinquennial periods [4,5,[22][23][24]. The number of tasks and the duration of a scheduled maintenance action are different from one case to another and depend on specific sub-assembly, components, manufacturer, model and capacity of the wind turbine.…”

Development of the future generation of smart high voltage connectors and related components for substations, with energy autonomy and wireless data transmission capability

“…Thus, it is highly desirable to acquire real-time data to monitor the current health status of power connectors [33]- [35] for a real-time diagnosis and to predict the failure in advance, while estimating the reliability and useful lifetime [36]- [44]. This approach allows optimizing the life cycle management by considering different aspects such as efficiency, power losses and costs points [45].…”