Statistical tools for prognostics and health management of complex systems
Abstract
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.
- Authors:
-
- Los Alamos National Laboratory
- Publication Date:
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1000935
- Report Number(s):
- LA-UR-10-01922; LA-UR-10-1922
TRN: US201101%%714
- DOE Contract Number:
- AC52-06NA25396
- Resource Type:
- Conference
- Resource Relation:
- Conference: 57th JANNAF Propulsion Meeting ; May 3, 2010 ; Colorado Springs, CO
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 42 ENGINEERING; 45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEFENSE; 97 MATHEMATICS AND COMPUTING; FORECASTING; MANAGEMENT; PROBABILITY; PROPULSION; RELIABILITY
Citation Formats
Collins, David H, Huzurbazar, Aparna V, and Anderson - Cook, Christine M. Statistical tools for prognostics and health management of complex systems. United States: N. p., 2010.
Web.
Collins, David H, Huzurbazar, Aparna V, & Anderson - Cook, Christine M. Statistical tools for prognostics and health management of complex systems. United States.
Collins, David H, Huzurbazar, Aparna V, and Anderson - Cook, Christine M. 2010.
"Statistical tools for prognostics and health management of complex systems". United States. https://www.osti.gov/servlets/purl/1000935.
@article{osti_1000935,
title = {Statistical tools for prognostics and health management of complex systems},
author = {Collins, David H and Huzurbazar, Aparna V and Anderson - Cook, Christine M},
abstractNote = {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.},
doi = {},
url = {https://www.osti.gov/biblio/1000935},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Jan 01 00:00:00 EST 2010},
month = {Fri Jan 01 00:00:00 EST 2010}
}