Prognostic Decision Making to extend a platform useful life under service constraint

Abstract

This paper adresses the problem of optimizing the useful life of a heterogeneous distributed platform which has to produce a given production service. The purpose is to provide a production scheduling that maximizes the production horizon. The use of Prognostics and Health Management (PHM) results in the form of Remaining Useful Life (RUL) allows to adapt the schedule to the wear and tear of equipment. This work comes within the scope of Prognostics Decision Making (DM). Each considered machine is supposed to be able to provide several throughputs corresponding to different operating conditions. The key point is to select the appropriate profile for each machine during the whole useful life of the platform. Many heuristics are proposed to cope with this decision problem and are compared through simulation results. Simulations assess the efficiency of these heuristics. Distance to the theoretical maximal value comes close to 10% for the most efficient ones. A repair module performing a revision of the schedules provided by the heuristics is moreover proposed to enhance the results. First results are promising.

A post-prognostics decision approach to optimize the commitment of fuel cell systems in stationary applications

“…Each maximal output reachable fmax j is updated as a function of the contribution of the corresponding machine, f j , following the relation defined in Equation (7).…”

“…A similar problem has been addressed in [7] and [8], where the purpose was to define a schedule of machines that maximizes the production horizon, based on the knowledge of each machine remaining useful life (RUL) in a PHM framework. In these studies, machine throughputs have been assumed to take discrete values.…”

Decision process to manage useful life of multi-stacks fuel cell systems under service constraint

“…Knowing this, the purpose is to select at each time which fuel cell stacks have to run and which power output has to be chosen for each of them to satisfy the load demand as long as possible. A similar problem has been addressed in [14] and [15] considering machines able to provide a discrete number of throughputs. It has been shown that a schedule can be adapted to machines health state by considering prognostics results in the form of RUL.…”