摘要：AbstractNowadays, several industries are digitizing their factories in the objective of creating smart factory as advocated by industry 4.0 paradigm. Prognostics and Health Management (PHM) concept is one of the main pillars of this transformation. Indeed, it aims at increasing industrial system reliability, availability and improving the company's competitiveness by promoting principles of failures anticipation, maintenance opportunities and proactivity. In that way, the main important processes of PHM are the Prognostics and (dynamic) Decision-Making. However, while many existing works focused on Prognostics (P) or on Decision Making (DM), very few are addressed the coupling between the P and DM (P&DM). The coupling is nevertheless fundamental when decisions have to be made with regards to very interrelated KPIs (e.g., product quality, energy performance) but also with regards to the anticipated evolution of these indicators, which will depend, at least, on the mission profile (e.g., load levels, durations) and the system degradation. So, this need of an efficient and relevant P&DM coupling for decision making is a scientific issue to be solved. In that way, this paper is defining and formalizing, through SysML diagrams, the concept of RPL (Residual Performance Lifetime) as a solution to support this P&DM coupling, mainly within the frame of Machine Tool application class. Indeed, this contribution is based on RENAULT's requirements and constraints to deploy the PHM and more specifically the P&DM on a large scale in its plants. This industrial context leads, in the paper, to instantiate and illustrate the RPL to a case of a specific machine tool called GROB.
