Acoustic Emission and Artificial Intelligence Procedure for Crack Source Localization
The acoustic emission (AE) technique is one of the most widely used in the field of structural monitoring. Its popularity mainly stems from the fact that it belongs to the category of non-destructive techniques (NDT) and allows the passive monitoring of structures. The technique employs piezoelectri...
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| Format: | Article |
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MDPI AG
2023-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/2/693 |
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| author | Jonathan Melchiorre Amedeo Manuello Bertetto Marco Martino Rosso Giuseppe Carlo Marano |
| author_facet | Jonathan Melchiorre Amedeo Manuello Bertetto Marco Martino Rosso Giuseppe Carlo Marano |
| author_sort | Jonathan Melchiorre |
| collection | DOAJ |
| description | The acoustic emission (AE) technique is one of the most widely used in the field of structural monitoring. Its popularity mainly stems from the fact that it belongs to the category of non-destructive techniques (NDT) and allows the passive monitoring of structures. The technique employs piezoelectric sensors to measure the elastic ultrasonic wave that propagates in the material as a result of the crack formation’s abrupt release of energy. The recorded signal can be investigated to obtain information about the source crack, its position, and its typology (Mode I, Mode II). Over the years, many techniques have been developed for the localization, characterization, and quantification of damage from the study of acoustic emission. The onset time of the signal is an essential information item to be derived from waveform analysis. This information combined with the use of the triangulation technique allows for the identification of the crack location. In the literature, it is possible to find many methods to identify, with increasing accuracy, the onset time of the P-wave. Indeed, the precision of the onset time detection affects the accuracy of identifying the location of the crack. In this paper, two techniques for the definition of the onset time of acoustic emission signals are presented. The first method is based on the Akaike Information Criterion (AIC) while the second one relies on the use of artificial intelligence (AI). A recurrent convolutional neural network (R-CNN) designed for sound event detection (SED) is trained on three different datasets composed of seismic signals and acoustic emission signals to be tested on a real-world acoustic emission dataset. The new method allows taking advantage of the similarities between acoustic emissions, seismic signals, and sound signals, enhancing the accuracy in determining the onset time. |
| first_indexed | 2024-03-09T11:18:23Z |
| format | Article |
| id | doaj.art-8ddb21c583b24e7092581e5b504e9442 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T11:18:23Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8ddb21c583b24e7092581e5b504e94422023-12-01T00:25:58ZengMDPI AGSensors1424-82202023-01-0123269310.3390/s23020693Acoustic Emission and Artificial Intelligence Procedure for Crack Source LocalizationJonathan Melchiorre0Amedeo Manuello Bertetto1Marco Martino Rosso2Giuseppe Carlo Marano3Department of Structural, Geotechnical and Building Engineering (DISEG), Politecnico di Torino, Corso Duca Degli Abruzzi, 24, 10128 Turin, ItalyDepartment of Structural, Geotechnical and Building Engineering (DISEG), Politecnico di Torino, Corso Duca Degli Abruzzi, 24, 10128 Turin, ItalyDepartment of Structural, Geotechnical and Building Engineering (DISEG), Politecnico di Torino, Corso Duca Degli Abruzzi, 24, 10128 Turin, ItalyDepartment of Structural, Geotechnical and Building Engineering (DISEG), Politecnico di Torino, Corso Duca Degli Abruzzi, 24, 10128 Turin, ItalyThe acoustic emission (AE) technique is one of the most widely used in the field of structural monitoring. Its popularity mainly stems from the fact that it belongs to the category of non-destructive techniques (NDT) and allows the passive monitoring of structures. The technique employs piezoelectric sensors to measure the elastic ultrasonic wave that propagates in the material as a result of the crack formation’s abrupt release of energy. The recorded signal can be investigated to obtain information about the source crack, its position, and its typology (Mode I, Mode II). Over the years, many techniques have been developed for the localization, characterization, and quantification of damage from the study of acoustic emission. The onset time of the signal is an essential information item to be derived from waveform analysis. This information combined with the use of the triangulation technique allows for the identification of the crack location. In the literature, it is possible to find many methods to identify, with increasing accuracy, the onset time of the P-wave. Indeed, the precision of the onset time detection affects the accuracy of identifying the location of the crack. In this paper, two techniques for the definition of the onset time of acoustic emission signals are presented. The first method is based on the Akaike Information Criterion (AIC) while the second one relies on the use of artificial intelligence (AI). A recurrent convolutional neural network (R-CNN) designed for sound event detection (SED) is trained on three different datasets composed of seismic signals and acoustic emission signals to be tested on a real-world acoustic emission dataset. The new method allows taking advantage of the similarities between acoustic emissions, seismic signals, and sound signals, enhancing the accuracy in determining the onset time.https://www.mdpi.com/1424-8220/23/2/693acoustic emissionartificial neural networkAkaike Information Criterion (AIC)source locationseismic signalscrack location |
| spellingShingle | Jonathan Melchiorre Amedeo Manuello Bertetto Marco Martino Rosso Giuseppe Carlo Marano Acoustic Emission and Artificial Intelligence Procedure for Crack Source Localization Sensors acoustic emission artificial neural network Akaike Information Criterion (AIC) source location seismic signals crack location |
| title | Acoustic Emission and Artificial Intelligence Procedure for Crack Source Localization |
| title_full | Acoustic Emission and Artificial Intelligence Procedure for Crack Source Localization |
| title_fullStr | Acoustic Emission and Artificial Intelligence Procedure for Crack Source Localization |
| title_full_unstemmed | Acoustic Emission and Artificial Intelligence Procedure for Crack Source Localization |
| title_short | Acoustic Emission and Artificial Intelligence Procedure for Crack Source Localization |
| title_sort | acoustic emission and artificial intelligence procedure for crack source localization |
| topic | acoustic emission artificial neural network Akaike Information Criterion (AIC) source location seismic signals crack location |
| url | https://www.mdpi.com/1424-8220/23/2/693 |
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