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

Niccolo Facciotto; Marcias Martinez; Enrico Troiani

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

Niccolo Facciotto, Marcias Martinez^*, Enrico Troiani

^*Corresponding author for this work

Structural Integrity & Composites

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

6 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 language	English
Title of host publication	Structural 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
Editors	F.K. Chang, F. Kopsaftopoulos
Publisher	Destech publications
Pages	1212-1219
Number of pages	8
Volume	1
ISBN (Electronic)	9781605953304
Publication status	Published - 2017
Event	11th International Workshop on Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance - Stanford, United States Duration: 12 Sep 2017 → 14 Sep 2017 Conference number: 11

Conference

Conference	11th International Workshop on Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance
Abbreviated title	IWSHM 2017
Country/Territory	United States
City	Stanford
Period	12/09/17 → 14/09/17

Cite this

Facciotto, N., Martinez, M., & Troiani, E. (2017). Source identification and classification of acoustic emission signals by a SHAZAM inspired pattern recognition algorithm. In F. K. Chang, & F. Kopsaftopoulos (Eds.), Structural 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 (Vol. 1, pp. 1212-1219). Destech publications.

Facciotto, Niccolo ; Martinez, Marcias ; Troiani, Enrico. / Source identification and classification of acoustic emission signals by a SHAZAM inspired pattern recognition algorithm. Structural 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. editor / F.K. Chang ; F. Kopsaftopoulos. Vol. 1 Destech publications, 2017. pp. 1212-1219

@inproceedings{da3da83ee95a4fd4a4bea7e5769395db,

title = "Source identification and classification of acoustic emission signals by a SHAZAM inspired pattern recognition algorithm",

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.",

author = "Niccolo Facciotto and Marcias Martinez and Enrico Troiani",

year = "2017",

language = "English",

volume = "1",

pages = "1212--1219",

editor = "F.K. Chang and F. Kopsaftopoulos",

booktitle = "Structural 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",

publisher = "Destech publications",

note = "11th International Workshop on Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance, IWSHM 2017 ; Conference date: 12-09-2017 Through 14-09-2017",

}

Facciotto, N, Martinez, M & Troiani, E 2017, Source identification and classification of acoustic emission signals by a SHAZAM inspired pattern recognition algorithm. in FK Chang & F Kopsaftopoulos (eds), Structural 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. vol. 1, Destech publications, pp. 1212-1219, 11th International Workshop on Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance, Stanford, United States, 12/09/17.

Source identification and classification of acoustic emission signals by a SHAZAM inspired pattern recognition algorithm. / Facciotto, Niccolo; Martinez, Marcias; Troiani, Enrico.

Structural 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. ed. / F.K. Chang; F. Kopsaftopoulos. Vol. 1 Destech publications, 2017. p. 1212-1219.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Source identification and classification of acoustic emission signals by a SHAZAM inspired pattern recognition algorithm

AU - Facciotto, Niccolo

AU - Martinez, Marcias

AU - Troiani, Enrico

N1 - Conference code: 11

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

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M3 - Conference contribution

AN - SCOPUS:85032445088

VL - 1

SP - 1212

EP - 1219

BT - Structural 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

A2 - Chang, F.K.

A2 - Kopsaftopoulos, F.

PB - Destech publications

T2 - 11th International Workshop on Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance

Y2 - 12 September 2017 through 14 September 2017

ER -

Facciotto N, Martinez M, Troiani E. Source identification and classification of acoustic emission signals by a SHAZAM inspired pattern recognition algorithm. In Chang FK, Kopsaftopoulos F, editors, Structural 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. Vol. 1. Destech publications. 2017. p. 1212-1219

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

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