An Adaptive Classifier for Detecting Helicopter Drivetrain Damage Using Acoustic Emission
This paper describes recent developments in a program to detect damage to helicopter drivetrains using acoustic emission (AE)1,2. Data obtained from an SH-60 drivetrain on an NAWC test stand was correlated with seeded fault damage in order to identify acoustic emission characteristics unique to the various sources. The objective is to extend prior work in applications of pattern recognition techniques and advanced machine intelligence to AE3,4,5 by designing and implementing an autonomous adaptive procedure to recognize and classify drivetrain damage from AE data.
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