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 which requires both a very precise modeling of the plant and a detailed analysis of all risks potentially incurred. In this paper, we wish to provide the reader with an overview of how advanced expert elicitation techniques can help identify, structure, quantify and feed sensitive data into a risk-based information system which can then be used for risk-based asset management evaluation. First we focus on the way knowledge management techniques allow EDF to structure and collect life-cycle management data into knowledge-based information systems. The elicitation of component experts is key, particularly in order to get technical information on the future behavior of the component (“anticipation” of whatever events may occur on the plant). We then detail how expert elicitations allow to quantify the probabilities of occurrence of the events: whether there is feedback data, models or not, expert opinion has to be taken into account and mixed, for instance with Bayesian procedures, to this information. Lastly we describe how the information elicited from experts can help top level decision makers get a transverse, long term view on how life management investment strategy translates into plant availability, avoided costs and improved component durability.

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