The life management of a nuclear power plant raises several major issues amongst which ranks the aging management of the key components of the plant, both from a technical and an economic point of view. Decision-makers are thus faced with the need to define the best strategy in order to achieve the best possible performance while meeting all regulatory requirements. EDF R&D is therefore deeply involved in developing advanced decision-making support tools so the EDF Engineering and Generation Divisions can optimize the long-term management of NPP components. In this paper we wish to provide the reader with an overview of how advanced information processing techniques such as signal and image processing algorithms and knowledge-based information systems recently contributed to the improvement of in-service inspections, condition-based maintenance, and asset management. First we focus on how multi-dimensional image reconstruction techniques increase component durability as they dramatically improve defect positioning and sizing when applied to radiographic data or ultrasonic data. We then detail current research work regarding the development of next generation prognostics systems which allow condition-based maintenance to take simultaneously into account monitoring data (for early fault detection), time-dependent aging models (for degradation kinetics), expert opinion as well as a quantified evaluation of the impact (on reliability and costs) of every potential decision. Lastly we describe how knowledge-based systems can help top level decision-makers get a transverse, long-term view on how a life-management investment strategy translates into plant availability, avoided costs and improved component durability.

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