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. This class of prognostic algorithms is termed Effects-Based or Type III Prognostics. Traditional individual- based prognostics involve identifying an appropriate degradation measure to characterize the system's progression to failure. These degradation measures may be sensed measurements, such as temperature or vibration level, or inferred measurements, such as model residuals or physics-based model predictions using other sensed measurements. Often, it is beneficial to combine several measures of degradation to develop a single parameter, called a prognostic parameter. A parametric model is fit to this parameter and then extrapolated to some predefined critical failure threshold to estimate the system's remaining useful life. Commonly, identification of a prognostic parameter is accomplished through visual inspection of the available information and engineering judgment. However, a set of metrics to characterize the suitability of prognostic parameters has been proposed. These metrics include monotonicity, prognosability, and trendability. Monotonicity characterizes a parameter's general increasing or decreasing nature. Prognosability measures the spread of the parameter's failure value for a population of systems. Finally, trendability indicates whether the parameters for a population of systems have the same underlying trend,and hence can be described by the same parametric function. This research formalizes these metrics in a way that is robust to the noise found in real world systems. The metrics are used in conjunction with a Genetic Algorithms optimization routine to identify an optimal prognostic parameter for the Prognostics and Health Management (PHM) Challenge data from the 2008 PHM conference.
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feature extraction, machine learning
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