This study focuses on the development of a source identification algorithm inspired by the SHAZAM music app. The algorithm makes use of a spectrogram analysis technique for distinguishing different Acoustic Emission (AE) events. The peaks of the spectrogram are used to obtain a constellation map generating a “fingerprint” like pattern for each acoustic emission source. The fingerprints are then used within an artificial intelligence algorithm as part of a Knowledge Discovery database. The database is then able to link the AE signal to a specific source type. An experimental program was developed to test the methodology. The results of this study demonstrate that signal sources can be classified and linked to specific emission types with a high level of accuracy.

Source Identification and Classification of Acoustic Emission Signals by a SHAZAM Inspired Pattern Recognition Algorithm / N. Facciotto; M. Martinez; E. Troiani. - ELETTRONICO. - (2017), pp. 1-8. (Intervento presentato al convegno 11th International Workshop on Structural Health Monitoring (IWSHM 2017) tenutosi a Stanford, CA, USA nel 12-14 September 2017) [10.12783/shm2017/13989].

Source Identification and Classification of Acoustic Emission Signals by a SHAZAM Inspired Pattern Recognition Algorithm

TROIANI, ENRICO
2017

Abstract

This study focuses on the development of a source identification algorithm inspired by the SHAZAM music app. The algorithm makes use of a spectrogram analysis technique for distinguishing different Acoustic Emission (AE) events. The peaks of the spectrogram are used to obtain a constellation map generating a “fingerprint” like pattern for each acoustic emission source. The fingerprints are then used within an artificial intelligence algorithm as part of a Knowledge Discovery database. The database is then able to link the AE signal to a specific source type. An experimental program was developed to test the methodology. The results of this study demonstrate that signal sources can be classified and linked to specific emission types with a high level of accuracy.
2017
Proceedings of International Workshop on Structural Health Monitoring (IWSHM 2017)
1
8
Source Identification and Classification of Acoustic Emission Signals by a SHAZAM Inspired Pattern Recognition Algorithm / N. Facciotto; M. Martinez; E. Troiani. - ELETTRONICO. - (2017), pp. 1-8. (Intervento presentato al convegno 11th International Workshop on Structural Health Monitoring (IWSHM 2017) tenutosi a Stanford, CA, USA nel 12-14 September 2017) [10.12783/shm2017/13989].
N. Facciotto; M. Martinez; E. Troiani
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/609058
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