Generalized Prognostics Algorithm Using Kalman Smoother. Issue 21 (2015)

• Record Type: Journal Article
• Authors: Bechhoefer, Eric; Schlanbusch, Rune
• Is Part Of: IFAC-PapersOnLine. Volume 48:Issue 21(2015)
• Page Start: 97
• Page End: 104
• Publication Date: 2015
• Subjects: Threshold Setting -- Prognostics -- Bearing Fault -- Remaining Useful Life; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2015.09.511
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: The ability to prognosticate the future state of a mechanical component can greatly improve the ability of a helicopter operator to manage their assets. Fundamentally, prognostics can change the logistics support of a helicopter by: reducing spares, improving the likelihood of a deployment meeting its mission requirements, and reducing unscheduled maintenance events. A successful prognosis is based on applying a fault model and usage of metrics (torque) to a diagnostic. This paper addresses a generalized fault and usage model through simplification of Paris' Law and the use of a Kalman Smoother. This state observer technique is a backward/forward filtering technique that has no phase delay. This allows a generalized, zero tuning model that provides an improved component health trend, and a better estimate of the current remaining useful life (RUL).