Acoustic Emission Source Localization with Generalized Regression Neural Network Based on Time Difference Mapping Method
Abstract Acoustic emission (AE) source localization is a powerful detection method. Time Difference Mapping (TDM) method is an effective method for detecting defects in complex structures. The core of this method is to search for a point with the minimum distance away from the verification point in...
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Gespeichert in:
| Autor*in: |
Liu, Z. H. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
Time difference mapping method |
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| Anmerkung: |
© Society for Experimental Mechanics 2020 |
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| Übergeordnetes Werk: |
Enthalten in: Experimental mechanics - Springer US, 1961, 60(2020), 5 vom: 02. März, Seite 679-694 |
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| Übergeordnetes Werk: |
volume:60 ; year:2020 ; number:5 ; day:02 ; month:03 ; pages:679-694 |
| Links: |
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| DOI / URN: |
10.1007/s11340-020-00591-8 |
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OLC2058189027 |
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| 520 | |a Abstract Acoustic emission (AE) source localization is a powerful detection method. Time Difference Mapping (TDM) method is an effective method for detecting defects in complex structures. The core of this method is to search for a point with the minimum distance away from the verification point in the time difference database. In Traditional Time Difference Mapping (T-TDM) method and Improved Time Difference Mapping (I-TDM) method, the larger database and denser grids allow the higher localization accuracy. If the location points are not included in the database, the localization accuracy of the T-TDM and I-TDM methods will be greatly affected. To solve the above problems, a new AE source localization method, Generalized Regression Neural Network Based on Time Difference Mapping (GRNN-TDM), is proposed to improve the localization accuracy in the study. In the proposed method, the time difference data of the sensor path on all nodes in the time difference mapping are used as the training input data and the coordinates of grid nodes are used as the training output data. After continuous learning and training, the neural network model predicts its possible source location with the time difference data collected from the verification point. In this paper, the localization of AE sources with T-TDM, I-TDM and GRNN-TDM methods was studied in four composite plates with different fiber layers and an aluminum plate with holes. The localization results showed that the localization accuracy of the GRNN-TDM method was higher than that of T-TDM and I-TDM methods. | ||
| 650 | 4 | |a Time difference mapping method | |
| 650 | 4 | |a Generalized regression neural network | |
| 650 | 4 | |a Acoustic emission | |
| 650 | 4 | |a Composite plate | |
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| 700 | 1 | |a Li, X. |4 aut | |
| 700 | 1 | |a He, C. F. |4 aut | |
| 700 | 1 | |a Wu, B. |4 aut | |
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10.1007/s11340-020-00591-8 doi (DE-627)OLC2058189027 (DE-He213)s11340-020-00591-8-p DE-627 ger DE-627 rakwb eng 690 VZ Liu, Z. H. verfasserin aut Acoustic Emission Source Localization with Generalized Regression Neural Network Based on Time Difference Mapping Method 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Society for Experimental Mechanics 2020 Abstract Acoustic emission (AE) source localization is a powerful detection method. Time Difference Mapping (TDM) method is an effective method for detecting defects in complex structures. The core of this method is to search for a point with the minimum distance away from the verification point in the time difference database. In Traditional Time Difference Mapping (T-TDM) method and Improved Time Difference Mapping (I-TDM) method, the larger database and denser grids allow the higher localization accuracy. If the location points are not included in the database, the localization accuracy of the T-TDM and I-TDM methods will be greatly affected. To solve the above problems, a new AE source localization method, Generalized Regression Neural Network Based on Time Difference Mapping (GRNN-TDM), is proposed to improve the localization accuracy in the study. In the proposed method, the time difference data of the sensor path on all nodes in the time difference mapping are used as the training input data and the coordinates of grid nodes are used as the training output data. After continuous learning and training, the neural network model predicts its possible source location with the time difference data collected from the verification point. In this paper, the localization of AE sources with T-TDM, I-TDM and GRNN-TDM methods was studied in four composite plates with different fiber layers and an aluminum plate with holes. The localization results showed that the localization accuracy of the GRNN-TDM method was higher than that of T-TDM and I-TDM methods. Time difference mapping method Generalized regression neural network Acoustic emission Composite plate Structural health monitoring Peng, Q. L. aut Li, X. aut He, C. F. aut Wu, B. aut Enthalten in Experimental mechanics Springer US, 1961 60(2020), 5 vom: 02. März, Seite 679-694 (DE-627)129593990 (DE-600)240480-1 (DE-576)015086852 0014-4851 nnns volume:60 year:2020 number:5 day:02 month:03 pages:679-694 https://doi.org/10.1007/s11340-020-00591-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-PHY AR 60 2020 5 02 03 679-694 |
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10.1007/s11340-020-00591-8 doi (DE-627)OLC2058189027 (DE-He213)s11340-020-00591-8-p DE-627 ger DE-627 rakwb eng 690 VZ Liu, Z. H. verfasserin aut Acoustic Emission Source Localization with Generalized Regression Neural Network Based on Time Difference Mapping Method 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Society for Experimental Mechanics 2020 Abstract Acoustic emission (AE) source localization is a powerful detection method. Time Difference Mapping (TDM) method is an effective method for detecting defects in complex structures. The core of this method is to search for a point with the minimum distance away from the verification point in the time difference database. In Traditional Time Difference Mapping (T-TDM) method and Improved Time Difference Mapping (I-TDM) method, the larger database and denser grids allow the higher localization accuracy. If the location points are not included in the database, the localization accuracy of the T-TDM and I-TDM methods will be greatly affected. To solve the above problems, a new AE source localization method, Generalized Regression Neural Network Based on Time Difference Mapping (GRNN-TDM), is proposed to improve the localization accuracy in the study. In the proposed method, the time difference data of the sensor path on all nodes in the time difference mapping are used as the training input data and the coordinates of grid nodes are used as the training output data. After continuous learning and training, the neural network model predicts its possible source location with the time difference data collected from the verification point. In this paper, the localization of AE sources with T-TDM, I-TDM and GRNN-TDM methods was studied in four composite plates with different fiber layers and an aluminum plate with holes. The localization results showed that the localization accuracy of the GRNN-TDM method was higher than that of T-TDM and I-TDM methods. Time difference mapping method Generalized regression neural network Acoustic emission Composite plate Structural health monitoring Peng, Q. L. aut Li, X. aut He, C. F. aut Wu, B. aut Enthalten in Experimental mechanics Springer US, 1961 60(2020), 5 vom: 02. März, Seite 679-694 (DE-627)129593990 (DE-600)240480-1 (DE-576)015086852 0014-4851 nnns volume:60 year:2020 number:5 day:02 month:03 pages:679-694 https://doi.org/10.1007/s11340-020-00591-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-PHY AR 60 2020 5 02 03 679-694 |
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10.1007/s11340-020-00591-8 doi (DE-627)OLC2058189027 (DE-He213)s11340-020-00591-8-p DE-627 ger DE-627 rakwb eng 690 VZ Liu, Z. H. verfasserin aut Acoustic Emission Source Localization with Generalized Regression Neural Network Based on Time Difference Mapping Method 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Society for Experimental Mechanics 2020 Abstract Acoustic emission (AE) source localization is a powerful detection method. Time Difference Mapping (TDM) method is an effective method for detecting defects in complex structures. The core of this method is to search for a point with the minimum distance away from the verification point in the time difference database. In Traditional Time Difference Mapping (T-TDM) method and Improved Time Difference Mapping (I-TDM) method, the larger database and denser grids allow the higher localization accuracy. If the location points are not included in the database, the localization accuracy of the T-TDM and I-TDM methods will be greatly affected. To solve the above problems, a new AE source localization method, Generalized Regression Neural Network Based on Time Difference Mapping (GRNN-TDM), is proposed to improve the localization accuracy in the study. In the proposed method, the time difference data of the sensor path on all nodes in the time difference mapping are used as the training input data and the coordinates of grid nodes are used as the training output data. After continuous learning and training, the neural network model predicts its possible source location with the time difference data collected from the verification point. In this paper, the localization of AE sources with T-TDM, I-TDM and GRNN-TDM methods was studied in four composite plates with different fiber layers and an aluminum plate with holes. The localization results showed that the localization accuracy of the GRNN-TDM method was higher than that of T-TDM and I-TDM methods. Time difference mapping method Generalized regression neural network Acoustic emission Composite plate Structural health monitoring Peng, Q. L. aut Li, X. aut He, C. F. aut Wu, B. aut Enthalten in Experimental mechanics Springer US, 1961 60(2020), 5 vom: 02. März, Seite 679-694 (DE-627)129593990 (DE-600)240480-1 (DE-576)015086852 0014-4851 nnns volume:60 year:2020 number:5 day:02 month:03 pages:679-694 https://doi.org/10.1007/s11340-020-00591-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-PHY AR 60 2020 5 02 03 679-694 |
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10.1007/s11340-020-00591-8 doi (DE-627)OLC2058189027 (DE-He213)s11340-020-00591-8-p DE-627 ger DE-627 rakwb eng 690 VZ Liu, Z. H. verfasserin aut Acoustic Emission Source Localization with Generalized Regression Neural Network Based on Time Difference Mapping Method 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Society for Experimental Mechanics 2020 Abstract Acoustic emission (AE) source localization is a powerful detection method. Time Difference Mapping (TDM) method is an effective method for detecting defects in complex structures. The core of this method is to search for a point with the minimum distance away from the verification point in the time difference database. In Traditional Time Difference Mapping (T-TDM) method and Improved Time Difference Mapping (I-TDM) method, the larger database and denser grids allow the higher localization accuracy. If the location points are not included in the database, the localization accuracy of the T-TDM and I-TDM methods will be greatly affected. To solve the above problems, a new AE source localization method, Generalized Regression Neural Network Based on Time Difference Mapping (GRNN-TDM), is proposed to improve the localization accuracy in the study. In the proposed method, the time difference data of the sensor path on all nodes in the time difference mapping are used as the training input data and the coordinates of grid nodes are used as the training output data. After continuous learning and training, the neural network model predicts its possible source location with the time difference data collected from the verification point. In this paper, the localization of AE sources with T-TDM, I-TDM and GRNN-TDM methods was studied in four composite plates with different fiber layers and an aluminum plate with holes. The localization results showed that the localization accuracy of the GRNN-TDM method was higher than that of T-TDM and I-TDM methods. Time difference mapping method Generalized regression neural network Acoustic emission Composite plate Structural health monitoring Peng, Q. L. aut Li, X. aut He, C. F. aut Wu, B. aut Enthalten in Experimental mechanics Springer US, 1961 60(2020), 5 vom: 02. März, Seite 679-694 (DE-627)129593990 (DE-600)240480-1 (DE-576)015086852 0014-4851 nnns volume:60 year:2020 number:5 day:02 month:03 pages:679-694 https://doi.org/10.1007/s11340-020-00591-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-PHY AR 60 2020 5 02 03 679-694 |
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10.1007/s11340-020-00591-8 doi (DE-627)OLC2058189027 (DE-He213)s11340-020-00591-8-p DE-627 ger DE-627 rakwb eng 690 VZ Liu, Z. H. verfasserin aut Acoustic Emission Source Localization with Generalized Regression Neural Network Based on Time Difference Mapping Method 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Society for Experimental Mechanics 2020 Abstract Acoustic emission (AE) source localization is a powerful detection method. Time Difference Mapping (TDM) method is an effective method for detecting defects in complex structures. The core of this method is to search for a point with the minimum distance away from the verification point in the time difference database. In Traditional Time Difference Mapping (T-TDM) method and Improved Time Difference Mapping (I-TDM) method, the larger database and denser grids allow the higher localization accuracy. If the location points are not included in the database, the localization accuracy of the T-TDM and I-TDM methods will be greatly affected. To solve the above problems, a new AE source localization method, Generalized Regression Neural Network Based on Time Difference Mapping (GRNN-TDM), is proposed to improve the localization accuracy in the study. In the proposed method, the time difference data of the sensor path on all nodes in the time difference mapping are used as the training input data and the coordinates of grid nodes are used as the training output data. After continuous learning and training, the neural network model predicts its possible source location with the time difference data collected from the verification point. In this paper, the localization of AE sources with T-TDM, I-TDM and GRNN-TDM methods was studied in four composite plates with different fiber layers and an aluminum plate with holes. The localization results showed that the localization accuracy of the GRNN-TDM method was higher than that of T-TDM and I-TDM methods. Time difference mapping method Generalized regression neural network Acoustic emission Composite plate Structural health monitoring Peng, Q. L. aut Li, X. aut He, C. F. aut Wu, B. aut Enthalten in Experimental mechanics Springer US, 1961 60(2020), 5 vom: 02. März, Seite 679-694 (DE-627)129593990 (DE-600)240480-1 (DE-576)015086852 0014-4851 nnns volume:60 year:2020 number:5 day:02 month:03 pages:679-694 https://doi.org/10.1007/s11340-020-00591-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-PHY AR 60 2020 5 02 03 679-694 |
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Acoustic Emission Source Localization with Generalized Regression Neural Network Based on Time Difference Mapping Method |
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(DE-627)OLC2058189027 (DE-He213)s11340-020-00591-8-p |
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Acoustic Emission Source Localization with Generalized Regression Neural Network Based on Time Difference Mapping Method |
| author_sort |
Liu, Z. H. |
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Experimental mechanics |
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Experimental mechanics |
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eng |
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600 - Technology |
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marc |
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2020 |
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679 |
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Liu, Z. H. Peng, Q. L. Li, X. He, C. F. Wu, B. |
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60 |
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690 VZ |
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Aufsätze |
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Liu, Z. H. |
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10.1007/s11340-020-00591-8 |
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690 |
| title_sort |
acoustic emission source localization with generalized regression neural network based on time difference mapping method |
| title_auth |
Acoustic Emission Source Localization with Generalized Regression Neural Network Based on Time Difference Mapping Method |
| abstract |
Abstract Acoustic emission (AE) source localization is a powerful detection method. Time Difference Mapping (TDM) method is an effective method for detecting defects in complex structures. The core of this method is to search for a point with the minimum distance away from the verification point in the time difference database. In Traditional Time Difference Mapping (T-TDM) method and Improved Time Difference Mapping (I-TDM) method, the larger database and denser grids allow the higher localization accuracy. If the location points are not included in the database, the localization accuracy of the T-TDM and I-TDM methods will be greatly affected. To solve the above problems, a new AE source localization method, Generalized Regression Neural Network Based on Time Difference Mapping (GRNN-TDM), is proposed to improve the localization accuracy in the study. In the proposed method, the time difference data of the sensor path on all nodes in the time difference mapping are used as the training input data and the coordinates of grid nodes are used as the training output data. After continuous learning and training, the neural network model predicts its possible source location with the time difference data collected from the verification point. In this paper, the localization of AE sources with T-TDM, I-TDM and GRNN-TDM methods was studied in four composite plates with different fiber layers and an aluminum plate with holes. The localization results showed that the localization accuracy of the GRNN-TDM method was higher than that of T-TDM and I-TDM methods. © Society for Experimental Mechanics 2020 |
| abstractGer |
Abstract Acoustic emission (AE) source localization is a powerful detection method. Time Difference Mapping (TDM) method is an effective method for detecting defects in complex structures. The core of this method is to search for a point with the minimum distance away from the verification point in the time difference database. In Traditional Time Difference Mapping (T-TDM) method and Improved Time Difference Mapping (I-TDM) method, the larger database and denser grids allow the higher localization accuracy. If the location points are not included in the database, the localization accuracy of the T-TDM and I-TDM methods will be greatly affected. To solve the above problems, a new AE source localization method, Generalized Regression Neural Network Based on Time Difference Mapping (GRNN-TDM), is proposed to improve the localization accuracy in the study. In the proposed method, the time difference data of the sensor path on all nodes in the time difference mapping are used as the training input data and the coordinates of grid nodes are used as the training output data. After continuous learning and training, the neural network model predicts its possible source location with the time difference data collected from the verification point. In this paper, the localization of AE sources with T-TDM, I-TDM and GRNN-TDM methods was studied in four composite plates with different fiber layers and an aluminum plate with holes. The localization results showed that the localization accuracy of the GRNN-TDM method was higher than that of T-TDM and I-TDM methods. © Society for Experimental Mechanics 2020 |
| abstract_unstemmed |
Abstract Acoustic emission (AE) source localization is a powerful detection method. Time Difference Mapping (TDM) method is an effective method for detecting defects in complex structures. The core of this method is to search for a point with the minimum distance away from the verification point in the time difference database. In Traditional Time Difference Mapping (T-TDM) method and Improved Time Difference Mapping (I-TDM) method, the larger database and denser grids allow the higher localization accuracy. If the location points are not included in the database, the localization accuracy of the T-TDM and I-TDM methods will be greatly affected. To solve the above problems, a new AE source localization method, Generalized Regression Neural Network Based on Time Difference Mapping (GRNN-TDM), is proposed to improve the localization accuracy in the study. In the proposed method, the time difference data of the sensor path on all nodes in the time difference mapping are used as the training input data and the coordinates of grid nodes are used as the training output data. After continuous learning and training, the neural network model predicts its possible source location with the time difference data collected from the verification point. In this paper, the localization of AE sources with T-TDM, I-TDM and GRNN-TDM methods was studied in four composite plates with different fiber layers and an aluminum plate with holes. The localization results showed that the localization accuracy of the GRNN-TDM method was higher than that of T-TDM and I-TDM methods. © Society for Experimental Mechanics 2020 |
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Acoustic Emission Source Localization with Generalized Regression Neural Network Based on Time Difference Mapping Method |
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Peng, Q. L. Li, X. He, C. F. Wu, B. |
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