Abstract:
Automated diagnosis systems are necessary for the maintenance of superannuated social infrastructure. This paper presents a methodology for detecting material defects usi...Show MoreMetadata
Abstract:
Automated diagnosis systems are necessary for the maintenance of superannuated social infrastructure. This paper presents a methodology for detecting material defects using acoustic signals in a hammering test. The approach comprises a feature extraction step using Short-Time Fourier Transform (STFT) and a classifier training step based on AdaBoost, an ensemble learning algorithm. Especially, we use weak learners based on a simple template matching method that can consider both the variable scale of amplitude and the variable frequency band. The experiments discriminate between defective and clean materials using different hammering test methods: rubbing and tapping.
Date of Conference: 11-13 September 2014
Date Added to IEEE Xplore: 26 January 2015
Electronic ISBN:978-1-4799-6968-5