Prognostics for Bus-Architecture Vehicles

Abstract

The goal of condition-based maintenance (CBM) is to optimize operational readiness of equipment through predictive and proactive maintenance. This capability requires accurate prediction of the remaining useful lifetime of each component, so that each may be replaced shortly before it is likely to impair mission readiness. By reducing unnecessary component replacements while still providing effective maintenance, effective Condition Based Maintenance ensures reliability of the warfighter's equipment at lessened expense, thus enhancing mission capability. The practice of forming predictions of component reliability is called prognostics. In this paper we consider the application of prognostics to military vehicles utilizing data bus architecture, particularly in the context of the Advanced Multiplex Test System (AMTS). We discuss several methods of designing prognostics (including incorporating the use of machine learning) as well as the barriers existing in this domain environment both to design and implementation of effective predictive maintenance. We present a variety of suggestions for a gradual approach to developing prognostics capabilities in this setting.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 2006
Accession Number
ADA481667

Entities

People

  • K. B. Capolongo
  • K. L. Johnsgard

Organizations

  • Western Oregon University

Tags

Communities of Interest

  • Autonomy
  • Biomedical
  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Aircraft Equipment
  • Artificial Intelligence
  • Condition Based Maintenance
  • Data Analysis
  • Diagnostic Equipment
  • Environment
  • Expert Systems
  • Learning
  • Machine Learning
  • Maintenance
  • Military Aircraft
  • Military Vehicles
  • Models
  • Operational Readiness
  • Probabilistic Models
  • Reliability
  • Vehicles

Fields of Study

  • Engineering

Readers

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  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference