Statistical tools for prognostics and health management of complex systems
- Los Alamos National Laboratory
Prognostics and Health Management (PHM) is increasingly important for understanding and managing today's complex systems. These systems are typically mission- or safety-critical, expensive to replace, and operate in environments where reliability and cost-effectiveness are a priority. We present background on PHM and a suite of applicable statistical tools and methods. Our primary focus is on predicting future states of the system (e.g., the probability of being operational at a future time, or the expected remaining system life) using heterogeneous data from a variety of sources. We discuss component reliability models incorporating physical understanding, condition measurements from sensors, and environmental covariates; system reliability models that allow prediction of system failure time distributions from component failure models; and the use of Bayesian techniques to incorporate expert judgments into component and system models.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC52-06NA25396
- OSTI ID:
- 1000935
- Report Number(s):
- LA-UR-10-01922; LA-UR-10-1922; TRN: US201101%%714
- Resource Relation:
- Conference: 57th JANNAF Propulsion Meeting ; May 3, 2010 ; Colorado Springs, CO
- Country of Publication:
- United States
- Language:
- English
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