A quantitative detection study was conducted on samples representative of lap joints in wing structure of the P-3 Orion airframe. These samples contained EDM notches in the bottom of the top layer and the top of the second layer, which radiated from ferrous fasteners holding the lap joint together. These aircraft are based on a damage tolerant design, so, consequently, defects would normally only make up a small fraction of the data (<= 5%). To detect the notches, a novel pulsed eddy current probe and robust cluster analysis were used to provide blind automatic detection of the notches without removal of the ferrous fasteners. Detection could then be confirmed using traditional bolt-hole eddy current. Because the samples are defect rich, statistical simulations were used to create samples more representative of what would be found on actual aircraft. Such modelling also allowed the effects of sample size and defect number to be investigated. With a false call rate of 10%, the a90/95 observed empirically was 2.1 mm (0.082 inch). This represents a marginal probability as the results indicate that other variables affect the POD. The technique did not require calibration as used in most eddy current inspections. In addition, sample statistics can be used to show that the assumptions of the technique (i.e. very small fraction of the fasteners have associated defects) are not violated and that the inspector performed the inspection in a suitable manner.

1.
D. M.
Butt
,
P. R.
Underhill
, and
T. W.
Krause
, “
Enhancing Pulsed Eddy Current for Inspection of P-3 Orion Lap-Joint Structures
,” in
AIP Conf. Proc.
1706
, pp.
90017
1
-7,
2016
.
2.
Stott
,
Colette
A.
,
Underhill
,
Peter
Ross
,
Babbar
,
Vijay
K.
and
Krause
,
Thomas
W.
Pulsed Eddy Current Detection of Cracks In Multilayer Aluminum Lap Joints
”,
IEEE Sensors Journal
,
15
(
2
),
956
962
, (
2015
).
3.
P.
Horan
,
P. R.
Underhill
, and
T. W.
Krause
, “
Pulsed eddy current detection of cracks in F/A-18 inner wing spar without wing skin removal using modified principal component analysis
,”
NDT & E Int.
,
55
(
1
),
21
27
, (
2013
).
4.
J.M.
Lattin
,
J.D.
Carroll
and
P.E.
Green
, “Analyzing Multivariate Data”,
Brooks/Cole
,
Toronto
,
2003
.
5.
De Maesschalck
,
R.
,
Jouan-Rimbaud
,
D.
and
Massart
,
D.L.
, “
Tutorial The Mahalanobis Distance
”,
Chemometrics and Intelligent Laboratory Systems
,
50
,
1
18
, (
2000
).
6.
Egan
,
William
, J. and
Morgan
,
Stephen
L.
, “
Outlier Detection in Multivariate Analytical Chemical Data
”,
Anal. Chem.
70
,
2372
2379
, (
1998
)
7.
Sergeant Mike
Bunn
,
Aerospace and Telecommunications Engineering Support Squadron
, Private Communication.
8.
Schneider
,
C. A.
,
Rasband
,
W. S.
&
Eliceiri
,
K. W.
, "
NIH Image to ImageJ: 25 years of image analysis
",
Nature methods
9
(
7
):
671
675
, (
2012
).
9.
Pison
G.
,
Van Aelst
S.
,
Willems
G.
“Small Sample Corrections for LTS and MCD” In:
Dutter
R.
,
Filzmoser
P.
,
Gather
U.
,
Rousseeuw
P.J.
(eds)
Developments in Robust Statistics. Physica
,
Heidelberg
,
2003
10.
Rousseeuw
P.J.
,
van Zomeren
B.C.
“Robust Distances: Simulations and Cutoff Values” In:
Directions in Robust Statistics and Diagnostics. The IMA Volumes in Mathematics and its Applications
, vol
34
.
Springer
,
New York, NY
,
1991
.
11.
Charles Annis
,
P.E.
, "
Statistical best-practices for building Probability of Detection (POD) models
"
R package mh1823, version 5.2.4
, http://StatisticalEngineering.com/mh1823/ 2016.
12.
MIL-HDBK-1823A, Department of Defense Handbook. Non Destructive Evaluation System Reliability Assessment, 7-April 2009.
13.
AFLCMC/EZ, Structures Bulletin “
In-Service Inspection Flaw Assumptions for Metallic Structures
”, EN-SB-08-012, Revision C, 23 May 2013
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