Quantifying expected gains from implementing a prognostics algorithm on systems with long logistics delay times

Many modern systems are themselves composed of smaller subsystems, which are composed of individual parts. Most prognostics algorithms attempt to provide a Remaining Useful Life (RUL) value for lower level components. Since the RUL prediction would allow for a replacement component to be requisitioned prior to a failure, it is anticipated the operational availability will be increased due to the reduction in system downtime attributed to logistics delay. For systems where logistics time is the major contributor to downtime, prognostics may yield large gains in operational availability. This paper provides an analysis of a hypothetical system that implements a prognostics algorithm and examines the system-level gains for different RUL prediction values. The analysis uses the BlockSim and a Monte Carlo-based simulation tool that is able to take multiple redundancies into account when calculating metrics. Finally, two metrics are proposed to help quantify the benefit of prognostics: Relative Downtime Reduction and Relative Availability Gain.