Integrated Blade Inspection System (IBIS) Upgrade Study

Abstract

The purpose of this design study was to identify ways to improve the Integrated Blade Inspection System. The Air Force requires inspection of jet engine compressor and turbine blades to locate defects and prevent engine failure. The current inspection process uses fluorescent penetrant as an aid to identify cracked blades. A systems engineering design process was applied to evaluate the current inspection techniques and to develop alternative methods to satisfy the Air Force requirements. Three different inspection systems were developed and compared to the current process: manual, semi-automated, and fully automated inspection. This study made several noteworthy contributions: development of classification software to validate the neural network approach for accurate blade classification, demonstration of potential advantages of charge-coupled device cameras for data gathering, quantification of the cost of incorrectly classifying jet engine blades, examination of the value of a statistical quality control plan for the inspection process, and identification of a method using multiple images to extract additional features from cracks. The study demonstrates that the fully automated system could dramatically outperform the manual inspection process by improving the consistency of the inspection process and raising the quality of the blades returned to service.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1992
Accession Number
ADA258912

Entities

People

  • Gregory J. Toussaint
  • John G. Snyder
  • Steven K. Saplin
  • Steven W. Perkins
  • Tony J. Deliberato

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies
  • Human Systems
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Computer Programming
  • Computer Vision
  • Computers
  • Control Systems
  • Data Processing
  • Digital Images
  • Electrical Engineering
  • Feature Extraction
  • Image Processing
  • Information Science
  • Maintenance
  • Pattern Recognition
  • Processing Equipment
  • Systems Engineering
  • Three Dimensional
  • Two Dimensional

Fields of Study

  • Engineering

Readers

  • Aerodynamics.
  • Computer Vision.
  • Software Engineering

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks