Fundamental Feature Extraction Methods for the Analysis of Eddy Current Data (Preprint)

Abstract

The objective of this paper is to explore features in eddy current data that are sensitive to defects in airframe structures while invariant to other noise factors commonly encountered in nondestructive evaluation (NDE). In particular, one goal is to detect and quantify corrosion-induced material loss in multi-layer aircraft structures. To investigate this problem, a series of eddy current studies were performed using an analytical model for varying total subsurface thickness loss (6%, 8%, and 10%), and percentage of the thickness loss occurring in the first or second layer (0, 25, 50, 75, and 100%). Results for the simulated studies with varying frequency are presented. A novel feature involving the first and second order derivatives of the real and imaginary parts of the impedance with respect to frequency is presented. Another goal is to detect and quantify subsurface cracks around fastener holes in structures. To investigate this problem, a series of studies are conducted using numerical models and then empirical data sets are analyzed to verify the viability of feature extraction techniques developed through the use of modeling. A feature sensitive to subsurface cracks around fastener holes is shown. Studies are conducted to show that this feature is invariant to irregular.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2006
Accession Number
ADA476705

Entities

People

  • Jeremy S. Knopp
  • John C. Aldrin

Tags

Communities of Interest

  • Advanced Electronics
  • Air Platforms

DTIC Thesaurus Topics

  • Air Force
  • Air Force Facilities
  • Air Force Research Laboratories
  • Corrosion
  • Data Analysis
  • Eddy Currents
  • Extraction
  • Fasteners
  • Feature Extraction
  • Frequency
  • Impedance
  • Materials
  • Measurement
  • Military Research
  • Signal Processing
  • Test And Evaluation
  • Thickness

Readers

  • Acoustical Oceanography.
  • Structural Health Monitoring of Composite Structures.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference