Identifying Suitable Degradation Parameters for Individual-Based Prognostics

Abstract

The ultimate goal of most prognostic systems is accurate prediction of the remaining useful life of individual systems or components based on their use and performance. Traditionally, individual-based prognostic methods use a measure of degradation to make lifetime estimates. Degradation measures may include sensed measurements, such as temperature or vibration level, or inferred measurements, such as model residuals or physics-based model predictions. Often, it is beneficial to combine several measures of degradation into a single parameter. Parameter features such as trendability, monotonicity, and prognosability can be used to compare candidate prognostic parameters to determine which is most useful for individual-based prognosis. By quantifying these features for a given parameter, the metrics can be used with any traditional optimization technique to identify an appropriate parameter. This parameter may be used with a parametric extrapolation model to make prognostic estimates for an individual unit. The proposed methods are illustrated with an application to simulated turbofan engine data.