DocumentCode :
1642555
Title :
Non-destructive testing of aerospace structures: granularity and data mining approach
Author :
Osegueda, R. ; Kreinovich, Vladik ; Potluri, Lakshmi ; Aló, Richard
Author_Institution :
FAST Center, Texas Univ., El Paso, TX, USA
Volume :
1
fYear :
2002
fDate :
6/24/1905 12:00:00 AM
Firstpage :
685
Lastpage :
689
Abstract :
For large aerospace structures, it is extremely important to detect faults, and nondestructive testing is the only practical way to do it. Based on measurements of ultrasonic waves, Eddy currents, magnetic resonance, etc., we reconstruct the locations of the faults. The best (most efficient) known statistical methods for fault reconstruction are not perfect. We show that the use of expert knowledge-based granulation improves the quality of fault reconstruction
Keywords :
aerospace testing; computational complexity; data mining; eddy current testing; expert systems; fault diagnosis; nondestructive testing; probability; statistical analysis; ultrasonic materials testing; Eddy currents; data mining; expert knowledge-based granulation; fault reconstruction; faults detection; granularity; large aerospace structures; magnetic resonance; nondestructive testing; ultrasonic waves; Aerospace testing; Current measurement; Data mining; Eddy currents; Fault detection; Magnetic resonance; Measurement standards; Nondestructive testing; Statistical analysis; Ultrasonic variables measurement;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems, 2002. FUZZ-IEEE'02. Proceedings of the 2002 IEEE International Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7280-8
Type :
conf
DOI :
10.1109/FUZZ.2002.1005075
Filename :
1005075
Link To Document :
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