Anomaly Detection through Grouping of SMD Machine Sounds Using Hierarchical Clustering
Surface-mounted device (SMD) assembly machines refer to production lines that assemble a variety of products that fit their purposes. As the required products become more diverse, models that oversee product anomaly detection are also becoming increasing linearly. In order to efficiently oversee pro...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/13/7569 |
| _version_ | 1797592127233851392 |
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| author | Young Jong Song Ki Hyun Nam Il Dong Yun |
| author_facet | Young Jong Song Ki Hyun Nam Il Dong Yun |
| author_sort | Young Jong Song |
| collection | DOAJ |
| description | Surface-mounted device (SMD) assembly machines refer to production lines that assemble a variety of products that fit their purposes. As the required products become more diverse, models that oversee product anomaly detection are also becoming increasing linearly. In order to efficiently oversee products, the number of models has to be reduced and products with similar characteristics have to be grouped and overseen. In this paper, we show that it is possible to handle a large number of new products using latent vectors obtained from the autoencoder model. By hierarchically clustering latent vectors, the model finds product groups with similar characteristics and oversees them by group. Furthermore, we validate our multi-product operation strategy for anomaly detection with a newly collected SMD dataset. Experimental results show that the anomaly detection method using hierarchical clustering of latent vectors is a practical management method for SMD anomaly detection. |
| first_indexed | 2024-03-11T01:47:02Z |
| format | Article |
| id | doaj.art-5b16a0314e114e4a9fe4761bdeba48b6 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T01:47:02Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-5b16a0314e114e4a9fe4761bdeba48b62023-11-18T16:08:17ZengMDPI AGApplied Sciences2076-34172023-06-011313756910.3390/app13137569Anomaly Detection through Grouping of SMD Machine Sounds Using Hierarchical ClusteringYoung Jong Song0Ki Hyun Nam1Il Dong Yun2Divsion of Computer Engineering, Hankuk University of Foreign Studies, Yongin 17035, Republic of KoreaElectrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of KoreaDivsion of Computer Engineering, Hankuk University of Foreign Studies, Yongin 17035, Republic of KoreaSurface-mounted device (SMD) assembly machines refer to production lines that assemble a variety of products that fit their purposes. As the required products become more diverse, models that oversee product anomaly detection are also becoming increasing linearly. In order to efficiently oversee products, the number of models has to be reduced and products with similar characteristics have to be grouped and overseen. In this paper, we show that it is possible to handle a large number of new products using latent vectors obtained from the autoencoder model. By hierarchically clustering latent vectors, the model finds product groups with similar characteristics and oversees them by group. Furthermore, we validate our multi-product operation strategy for anomaly detection with a newly collected SMD dataset. Experimental results show that the anomaly detection method using hierarchical clustering of latent vectors is a practical management method for SMD anomaly detection.https://www.mdpi.com/2076-3417/13/13/7569anomaly detectionautoencoderhierarchical clustering |
| spellingShingle | Young Jong Song Ki Hyun Nam Il Dong Yun Anomaly Detection through Grouping of SMD Machine Sounds Using Hierarchical Clustering Applied Sciences anomaly detection autoencoder hierarchical clustering |
| title | Anomaly Detection through Grouping of SMD Machine Sounds Using Hierarchical Clustering |
| title_full | Anomaly Detection through Grouping of SMD Machine Sounds Using Hierarchical Clustering |
| title_fullStr | Anomaly Detection through Grouping of SMD Machine Sounds Using Hierarchical Clustering |
| title_full_unstemmed | Anomaly Detection through Grouping of SMD Machine Sounds Using Hierarchical Clustering |
| title_short | Anomaly Detection through Grouping of SMD Machine Sounds Using Hierarchical Clustering |
| title_sort | anomaly detection through grouping of smd machine sounds using hierarchical clustering |
| topic | anomaly detection autoencoder hierarchical clustering |
| url | https://www.mdpi.com/2076-3417/13/13/7569 |
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