Generalized Prognostics Algorithm Using Kalman Smoother

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).