Linguistic Model for Engine Power Loss

Abstract

Army ground vehicles often operate in extremely severe environmental and battlefield conditions. Condition Based Maintenance (CBM) allows maintenance to be performed based on evidence of need provided by reliability modeling and/or other enabling technologies, thus reducing maintenance costs and increasing vehicle availability. A Takagi-Sugeno fuzzy model is developed to diagnose the loss of engine power of light trucks. Baseline data are acquired through engine performance measurements. The Adaptive Neuro-Fuzzy (ANFIS) training method is used to extract the fuzzy rules. To improve the quality of the model a combination of the least-square error and the backpropagation gradient descent methods is implemented to minimize the errors.

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Document Details

Document Type
Technical Report
Publication Date
Nov 27, 2011
Accession Number
ADA577121

Entities

People

  • Bradley Bazuin
  • Claudia Fajardo
  • Janos Grantner
  • Jumana Al-shawawreh
  • Liang Dong
  • Matthew P. Castanier
  • Richard Hathaway
  • Shabbir Hussain

Organizations

  • United States Army Tank Automotive Research, Development and Engineering Center

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Base Lines
  • Combustion
  • Computers
  • Condition Based Maintenance
  • Data Storage Systems
  • Diesel Engines
  • Engineering
  • Engines
  • Failure Mode And Effect Analysis
  • Fuzzy Logic
  • Ground Vehicles
  • Measurement
  • Mechanical Engineering
  • Reliability
  • Simulations
  • United States Government
  • Vehicles

Fields of Study

  • Engineering

Readers

  • Artificial Intelligence
  • Logistics and Supply Chain Management.
  • Neural Network Machine Learning.