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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