Upgrading Engine Test Cells for Improved Troubleshooting and Diagnostics

Abstract

Upgrading military engine test cells with advanced diagnostic and troubleshooting capabilities will play a critical role in increasing aircraft availability and test cell effectiveness while simultaneously reducing engine opening and maintenance costs. Sophisticated performance and mechanical anomaly detection and fault classification algorithms utilizing thermodynamic, statistical, and empirical engine models are now being implmented as part of a United States Air Force Advanced Test Cell Upgrade Initiative. Under this program, a comprehensive set of real- time and post-test diagnostic software modules, including sensor validation algorithms, performance fault classification techniques and vibration feature analysis are being developed. An automated troubleshooting guide is also being implemented to streamline the troubleshooting process for both inexperienced and experienced technicians. This artificial intelligence based tool enhances the conventional troubleshooting tree architecture by incorporating probability of occurrence statistics to optimize the troubleshooting path. This paper describes the development and implementation of the F404 engine test cell upgrade at the Jacksonville Naval Air Station.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2002
Accession Number
ADA408947

Entities

People

  • James Scheid
  • Michael J. Roemer
  • Michael Schoeller
  • Richard Friend
  • Rolf F. Orsagh

Tags

Communities of Interest

  • Air Platforms
  • Human Systems
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Accuracy
  • Air Force
  • Change Detection
  • Data Acquisition
  • Databases
  • Detection
  • Failure Mode And Effect Analysis
  • Fixed Wing Aircraft
  • Gas Turbines
  • Information Science
  • Measurement
  • Operating Systems
  • Pattern Recognition
  • Propulsion Systems
  • Reliability
  • Signal Processing
  • Turbines

Readers

  • Aerospace Test and Evaluation
  • Neural Network Machine Learning.
  • Software Engineering.

Technology Areas

  • AI & ML