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Automated diagnosis of material condition in hammering test using a boosting algorithm | IEEE Conference Publication | IEEE Xplore

Automated diagnosis of material condition in hammering test using a boosting algorithm


Abstract:

Automated diagnosis systems are necessary for the maintenance of superannuated social infrastructure. This paper presents a methodology for detecting material defects usi...Show More

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

ISSN Information:

Conference Location: Evanston, IL, USA

References

References is not available for this document.