Task scheduling to extend platform useful life using prognostics.

International audienceIn this paper, we aim at maximizing the useful life of a heterogeneous distributed platform which has to deliver a given production. The machines (one nominal mode and several degraded ones). Depending on the profile, a machine reaches a given throughput. At each time the sum of the machine throughputs that are currentky running determines the global throughput. Moreover, each machine is supposed to be monitored and a prognostic module gives its remaining useful life depending on both its past and future usage (profile). the objective is to configure the platform so as to reach the demand as long as possible. We propose to discretize the time into periods and to choose a configuration for each period. We propose an Integer Linear Programming (ILP) model to find such configurations for a fixed time horizon. Due to the number of variables and constraints in the ILP, the largest horizon can be computed for small instances of the problem. For larger ones , we propose polynomial time heuristics to maximize the useful life. Exhaustive simulations show that the heuristics solutions are close to the optimal (5% in average) in the case where the optimal horizon can to computed. for other platforms with a very large number of machines, simulations assess the efficienty of our heuristics. The distance to the theoretical maximal value is about 8% in average

Integrating Structural Health Management with Contingency Control for Wind Turbines

Maximizing turbine up-time and reducing maintenance costs are key technology drivers for wind turbine operators. Components within wind turbines are subject to considerable stresses due to unpredictable environmental conditions resulting from rapidly changing local dynamics. In that context, systems health management has the aim to assess the state-of-health of components within a wind turbine, to estimate remaining life, and to aid in autonomous decision-making to minimize damage to the turbine. Advanced contingency control is one way to enable autonomous decision-making by providing the mechanism to enable safe and efficient turbine operation. The work reported herein explores the integration of condition monitoring of wind turbine blades with contingency control to balance the trade-offs between maintaining system health and energy capture. Results are demonstrated using a high fidelity simulator of a utility-scale wind turbine