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Title: 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:
 [1];  [1];  [1]
  1. 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}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

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