Source identification and classification of acoustic emission signals by a SHAZAM inspired pattern recognition algorithm

Niccolo Facciotto, Marcias Martinez, Enrico Troiani

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationStructural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance - Proceedings of the 11th International Workshop on Structural Health Monitoring, IWSHM 2017
EditorsF.K. Chang, F. Kopsaftopoulos
PublisherDestech publications
Pages1212-1219
Number of pages8
Volume1
ISBN (Electronic)9781605953304
Publication statusPublished - 2017
Event11th International Workshop on Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance - Stanford, United States
Duration: 12 Sep 201714 Sep 2017
Conference number: 11

Conference

Conference11th International Workshop on Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance
Abbreviated titleIWSHM 2017
CountryUnited States
CityStanford
Period12/09/1714/09/17

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