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Title: Algorithm development for Prognostics and Health Management (PHM).

Abstract

This report summarizes the results of a three-year LDRD project on prognostics and health management. System failure over some future time interval (an alternative definition is the capability to predict the remaining useful life of a system). Prognostics are integrated with health monitoring (through inspections, sensors, etc.) to provide an overall PHM capability that optimizes maintenance actions and results in higher availability at a lower cost. Our goal in this research was to develop PHM tools that could be applied to a wide variety of equipment (repairable, non-repairable, manufacturing, weapons, battlefield equipment, etc.) and require minimal customization to move from one system to the next. Thus, our approach was to develop a toolkit of reusable software objects/components and architecture for their use. We have developed two software tools: an Evidence Engine and a Consequence Engine. The Evidence Engine integrates information from a variety of sources in order to take into account all the evidence that impacts a prognosis for system health. The Evidence Engine has the capability for feature extraction, trend detection, information fusion through Bayesian Belief Networks (BBN), and estimation of remaining useful life. The Consequence Engine involves algorithms to analyze the consequences of various maintenance actions. The Consequencemore » Engine takes as input a maintenance and use schedule, spares information, and time-to-failure data on components, then generates maintenance and failure events, and evaluates performance measures such as equipment availability, mission capable rate, time to failure, and cost. This report summarizes the capabilities we have developed, describes the approach and architecture of the two engines, and provides examples of their use. 'Prognostics' refers to the capability to predict the probability of« less

Authors:
; ; ;
Publication Date:
Research Org.:
Sandia National Laboratories
Sponsoring Org.:
USDOE
OSTI Identifier:
918353
Report Number(s):
SAND2003-3820
TRN: US200818%%236
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 42 ENGINEERING; ALGORITHMS; EQUIPMENT; MAINTENANCE; SERVICE LIFE; MONITORING; COMPUTERIZED SIMULATION; FAILURES; FORECASTING

Citation Formats

Swiler, Laura Painton, Campbell, James E, Doser, Adele Beatrice, and Lowder, Kelly S. Algorithm development for Prognostics and Health Management (PHM).. United States: N. p., 2003. Web. doi:10.2172/918353.
Swiler, Laura Painton, Campbell, James E, Doser, Adele Beatrice, & Lowder, Kelly S. Algorithm development for Prognostics and Health Management (PHM).. United States. https://doi.org/10.2172/918353
Swiler, Laura Painton, Campbell, James E, Doser, Adele Beatrice, and Lowder, Kelly S. Wed . "Algorithm development for Prognostics and Health Management (PHM).". United States. https://doi.org/10.2172/918353. https://www.osti.gov/servlets/purl/918353.
@article{osti_918353,
title = {Algorithm development for Prognostics and Health Management (PHM).},
author = {Swiler, Laura Painton and Campbell, James E and Doser, Adele Beatrice and Lowder, Kelly S},
abstractNote = {This report summarizes the results of a three-year LDRD project on prognostics and health management. System failure over some future time interval (an alternative definition is the capability to predict the remaining useful life of a system). Prognostics are integrated with health monitoring (through inspections, sensors, etc.) to provide an overall PHM capability that optimizes maintenance actions and results in higher availability at a lower cost. Our goal in this research was to develop PHM tools that could be applied to a wide variety of equipment (repairable, non-repairable, manufacturing, weapons, battlefield equipment, etc.) and require minimal customization to move from one system to the next. Thus, our approach was to develop a toolkit of reusable software objects/components and architecture for their use. We have developed two software tools: an Evidence Engine and a Consequence Engine. The Evidence Engine integrates information from a variety of sources in order to take into account all the evidence that impacts a prognosis for system health. The Evidence Engine has the capability for feature extraction, trend detection, information fusion through Bayesian Belief Networks (BBN), and estimation of remaining useful life. The Consequence Engine involves algorithms to analyze the consequences of various maintenance actions. The Consequence Engine takes as input a maintenance and use schedule, spares information, and time-to-failure data on components, then generates maintenance and failure events, and evaluates performance measures such as equipment availability, mission capable rate, time to failure, and cost. This report summarizes the capabilities we have developed, describes the approach and architecture of the two engines, and provides examples of their use. 'Prognostics' refers to the capability to predict the probability of},
doi = {10.2172/918353},
url = {https://www.osti.gov/biblio/918353}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2003},
month = {10}
}