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Wednesday, May 15 • 2:00pm - 2:30pm
Evaluating the Accuracy of Prognostic Estimates: Remaining Useful Life, State of Health, Prognostic Horizon

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This paper describes how to evaluate the accuracy of prognostic estimates for remaining-useful life (RUL), state of health (SoH), and prognostic horizon (PH). The primary goal of prognostics is to be able to accurately predict a future failure in systems, including condition-based-maintenance (CBM) systems that use condition-based data (CBD) to detect degradation and project that degradation to a failing level at a future time. That future time and the time when the data is sampled are used to calculate predictive (prognostic) information such as RUL, SoH, and PH. Evaluating the accuracy of prognostic information is critical and begins with knowing that ideal, zero-error estimates are not practical, primarily of factors such as the following: how long it takes for degradation to progress to a failing level, when the onset of degradation occurs, changes in rate of degradation due to operating and environment conditions, and noise in the sensor-measurement system. Performance metrics to evaluate accuracy are developed and presented: convergence efficiency, prognostic distance (PD), and prognostic-horizon accuracy (PH).

Speakers
avatar for James Hofmeister

James Hofmeister

Distinguished Engineer, Ridgetop Group, Inc.


Wednesday May 15, 2019 2:00pm - 2:30pm
Freedom Ballroom I