Deep Learning-Based Feature Extraction of Acoustic Emission Signals for Monitoring Wear of Grinding Wheels
Tool wear monitoring is a critical issue in advanced manufacturing systems. In the search for sensing devices that can provide information about the grinding process, Acoustic Emission (AE) appears to be a promising technology. The present paper presents a novel deep learning-based proposal for grin...
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MDPI AG
2022-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/18/6911 |
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| author | D. González J. Alvarez J. A. Sánchez L. Godino I. Pombo |
| author_facet | D. González J. Alvarez J. A. Sánchez L. Godino I. Pombo |
| author_sort | D. González |
| collection | DOAJ |
| description | Tool wear monitoring is a critical issue in advanced manufacturing systems. In the search for sensing devices that can provide information about the grinding process, Acoustic Emission (AE) appears to be a promising technology. The present paper presents a novel deep learning-based proposal for grinding wheel wear status monitoring using an AE sensor. The most relevant finding is the possibility of efficient feature extraction form frequency plots using CNNs. Feature extraction from FFT plots requires sound domain-expert knowledge, and thus we present a new approach to automated feature extraction using a pre-trained CNN. Using the features extracted for different industrial grinding conditions, t-SNE and PCA clustering algorithms were tested for wheel wear state identification. Results are compared for different industrial grinding conditions. The initial state of the wheel, resulting from the dressing operation, is clearly identified for all the experiments carried out. This is a very important finding, since dressing strongly affects operation performance. When grinding parameters produce acute wear of the wheel, the algorithms show very good clustering performance using the features extracted by the CNN. Performance of both t-SNE and PCA was very much the same, thus confirming the excellent efficiency of the pre-trained CNN for automated feature extraction from FFT plots. |
| first_indexed | 2024-03-09T22:33:39Z |
| format | Article |
| id | doaj.art-390f6d640c4247e68b349d5a27af43b5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T22:33:39Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-390f6d640c4247e68b349d5a27af43b52023-11-23T18:51:20ZengMDPI AGSensors1424-82202022-09-012218691110.3390/s22186911Deep Learning-Based Feature Extraction of Acoustic Emission Signals for Monitoring Wear of Grinding WheelsD. González0J. Alvarez1J. A. Sánchez2L. Godino3I. Pombo4Department of Mechanical Engineering, Faculty of Engineering of Bilbao, University of the Basque Country (UPV/EHU), 48013 Bilbao, SpainIdeko Centro Tecnológico, Basque Research and Technology Alliance (BRTA), 20870 Elgoibar, SpainDepartment of Mechanical Engineering, Faculty of Engineering of Bilbao, University of the Basque Country (UPV/EHU), 48013 Bilbao, SpainDepartment of Mechanical Engineering, Faculty of Engineering of Bilbao, University of the Basque Country (UPV/EHU), 48013 Bilbao, SpainDepartment of Mechanical Engineering, Faculty of Engineering of Bilbao, University of the Basque Country (UPV/EHU), 48013 Bilbao, SpainTool wear monitoring is a critical issue in advanced manufacturing systems. In the search for sensing devices that can provide information about the grinding process, Acoustic Emission (AE) appears to be a promising technology. The present paper presents a novel deep learning-based proposal for grinding wheel wear status monitoring using an AE sensor. The most relevant finding is the possibility of efficient feature extraction form frequency plots using CNNs. Feature extraction from FFT plots requires sound domain-expert knowledge, and thus we present a new approach to automated feature extraction using a pre-trained CNN. Using the features extracted for different industrial grinding conditions, t-SNE and PCA clustering algorithms were tested for wheel wear state identification. Results are compared for different industrial grinding conditions. The initial state of the wheel, resulting from the dressing operation, is clearly identified for all the experiments carried out. This is a very important finding, since dressing strongly affects operation performance. When grinding parameters produce acute wear of the wheel, the algorithms show very good clustering performance using the features extracted by the CNN. Performance of both t-SNE and PCA was very much the same, thus confirming the excellent efficiency of the pre-trained CNN for automated feature extraction from FFT plots.https://www.mdpi.com/1424-8220/22/18/6911deep learningacoustic emissiongrindingfeature extraction |
| spellingShingle | D. González J. Alvarez J. A. Sánchez L. Godino I. Pombo Deep Learning-Based Feature Extraction of Acoustic Emission Signals for Monitoring Wear of Grinding Wheels Sensors deep learning acoustic emission grinding feature extraction |
| title | Deep Learning-Based Feature Extraction of Acoustic Emission Signals for Monitoring Wear of Grinding Wheels |
| title_full | Deep Learning-Based Feature Extraction of Acoustic Emission Signals for Monitoring Wear of Grinding Wheels |
| title_fullStr | Deep Learning-Based Feature Extraction of Acoustic Emission Signals for Monitoring Wear of Grinding Wheels |
| title_full_unstemmed | Deep Learning-Based Feature Extraction of Acoustic Emission Signals for Monitoring Wear of Grinding Wheels |
| title_short | Deep Learning-Based Feature Extraction of Acoustic Emission Signals for Monitoring Wear of Grinding Wheels |
| title_sort | deep learning based feature extraction of acoustic emission signals for monitoring wear of grinding wheels |
| topic | deep learning acoustic emission grinding feature extraction |
| url | https://www.mdpi.com/1424-8220/22/18/6911 |
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