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PAPER ABSTRACT
Artificial Neural Networks in Engineering - 1995
ISBN # 0-7918-0048-2
This paper is copyrighted by ASME Press. For reprints,
please contact the ANNIE organizers at http://www.umr.edu/~annie/
or ASME Press at The American Society of Mechanical Engineers, 345 East 47th Street, New
York, NY 10017, USA.
DETECTING CRACK GROWTH IN METAL STRUCTURES USING
TEMPORAL PROCESSING AND THE PARAMETRIC AVALANCHE STOCHASTIC FILTER NEURAL NETWORK
HARVEY L. BODINE, ROBERT L. DAWES, STEVEN S. HENLEY
Martingale Research Corporation, Richardson, TX 75080
ERIC V. K. HILL
Aerospace Engineering Department, Embry-Riddle Aeronautical University, Daytona
Beach, FL 32114-3900
ABSTRACT:
In this paper we discuss the lessons learned from applying a novel
neural network architecture, the Parametric Avalanche Stochastic Filter (PASF) to a
complex nondestructive testing and signal processing problem. We addressed the problem of
identifying crack growth in complex metal structures using acoustic emissions. To test our
approach, multiple aluminum pressure vessels, simulating an aircraft pressure hull, were
constructed, instrumented, and fatigued-cycled to destruction. By combining the PASF
neural network and processing techniques from speech recognition, we achieved
classification rates of up to 93% for crack growth in the presence of rivet noise,
rubbing, membrane noise, and other clutter.
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