Prognostics and advanced diagnostics for improving steady-state and pulse reliability
暂无分享,去创建一个
Achieving high reliability of military platforms is critical for reducing operating costs and increasing operational availability and combat readiness. This paper describes results of a comparative analysis of two approaches to improving platform reliability. The first is based on platform inspections (performed at specific time intervals) and replacement of parts whose remaining life is found to be limited. By selecting appropriate inspection time intervals and fraction of remaining life left, various levels of platform reliability are achieved. The second approach utilizes prognostics capability, which provides estimates of remaining useful life based on platform's health state and the upcoming mission duration. In the analysis, the US Army Abrams M1A1 tank reliability data are utilized. Two operational regimes are considered. A steady-state situation develops after the tanks have been operating sufficiently long, and the average operational availability is not a strong function of time. In contrast, pulse reliability is achieved during a mission of limited duration (pulse). Such situations arise, for example, when tanks are selected for a specific mission that is limited in time, but during which repair or replacement of parts is not possible and high platform reliability is required. The presented results can be used for evaluating the benefits of prognostics approach, determining intelligent maintenance procedures, and developing effective strategies for achieving improved operational readiness and required pulse reliability
[1] John A. Wilhelm. Analysis of optimum depot level component replacement policy for retrograded M1 Abrams tanks , 1990 .
[2] Eric Peltz. Equipment Sustainment Requirements for the Transforming Army , 2004 .
[3] P. Koehn,et al. Improving reliability and operational availability of military systems , 2005, 2005 IEEE Aerospace Conference.
[4] Eric Peltz,et al. Diagnosing the Army's Equipment Readiness: The Equipment Downtime Analyzer , 2002 .