Statistical Methods for Automatic Crack Detection Based on Vibrothermography Sequence-of-Images Data

Date
2010-06-01
Authors
Holland, Stephen
Li, Ming
Holland, Stephen
Meeker, William
Meeker, William
Major Professor
Advisor
Committee Member
Journal Title
Journal ISSN
Volume Title
Publisher
Altmetrics
Authors
Research Projects
Organizational Units
Statistics
Organizational Unit
Journal Issue
Series
Department
Statistics
Abstract

Vibrothermography is a relatively new nondestructive evaluation technique for finding cracks through frictional heat generated from crack surface vibrations under external excitations. The vibrothermography inspection method provides a sequence of infrared images as output. We use a matched filter technique to increase the signal-to-noise ratio of the sequence-of-images data. An automatic crack detection criterion based on the features extracted from the matched filter output greatly increases the sensitivity of the vibrothermography inspection method. In this paper, we develop a three dimensional matched filter for the sequence-of-images data, present the statistical analysis for the matched filter output, and evaluate the probability of detection. Our results show the crack detection criterion based on the matched filter output provides improved detection capability.

Comments

This preprint was published as M. Li, S.D. Holland and W.Q. Meeker, "Statistical Methods for Automatic Crack Detection Based on Vibrothermography Sequence-of-Images Data", Applied Stochastic Models in Business and Industry (2010): 509-512, doi: 10.1002/asmb.867.

Description
Keywords
Citation
DOI
Source
Collections