Prescription Based Maintenance Management System

Abstract

In recent years, significant focus has been placed on the development and implementation of advanced prognostic and health management (PHM) technologies in military and industrial applications. The term PHM encompasses anomaly, diagnostic and prognostic algorithms as well as higher level reasoning algorithms for isolating root causes of faults/failures and directing optimal operational or maintenance actions. In such systems, two current deficiencies exist. First, for a variety of reasons, component and subsystem interactions in such systems are poorly realized. The issue manifests itself as multiple dependent "boxes" indicating faults with shotgun tests or valuable domain expertise required to de-conflict and reduce ambiguity groups. Secondly, complex systems still largely rely on expert rule-bases for reasoning which are notoriously difficult to maintain over a life cycle and are prone to logical conflicts. This paper begins to address these deficiencies by outlining a simulation-based process for automatically: (1) realizing complex system interactions for optimal PHM system design and (2) building and maintaining model-based reasoning architectures where decisions and conclusions naturally precipitate out of a more manageable system model.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2007
Accession Number
ADA497979

Entities

People

  • G. S. Valentine
  • John Scharschan
  • Thomas Galie

Tags

Communities of Interest

  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Algorithms
  • Application Software
  • Artificial Intelligence Computing
  • Computer Programming
  • Databases
  • Detectors
  • Failure Mode And Effect Analysis
  • False Alarms
  • Graphical User Interface
  • Information Systems
  • Machine Learning
  • Maintenance
  • Maintenance Management
  • Neural Networks
  • Open System Architecture
  • Reinforcement Learning
  • Standards

Fields of Study

  • Engineering

Readers

  • Artificial Intelligence
  • Educational Psychology
  • Software Engineering.