Case Study for New Feature Extraction Algorithms, Automated Data Classification, and Model-Assisted Probability of Detection Evaluation (Preprint)

Abstract

This paper explores feature extraction algorithms for crack characterization in eddy current inspection of fastener sites. A novel feature extraction method fitting approximate models to data associated with geometric part features addressing adjacent fastener sites and panel edges are developed. Data classification methods in the circumferential direction around fastener sites are developed to better characterize fatigue cracks with improved noise invariance. Model-assisted probability of detection results are presented highlighting the benefit of automation in NDE.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2006
Accession Number
ADA464672

Entities

People

  • J. C. Aldrin
  • J. S. Knopp

Tags

Communities of Interest

  • Air Platforms

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Case Studies
  • Classification
  • Computational Science
  • Computer Programs
  • Computer Simulations
  • Data Analysis
  • Detection
  • Eddy Currents
  • Experimental Data
  • Extraction
  • Fasteners
  • Feature Extraction
  • Probability
  • Test And Evaluation

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Radar Systems Engineering.
  • Structural Health Monitoring of Composite Structures.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference