A Novel FS-GAN-Based Anomaly Detection Approach for Smart Manufacturing
In this study, we present the few-shot generative adversarial network (FS-GAN) model, which integrates few-shot learning and a generative adversarial network with an unsupervised learning approach (AnoGAN) to address the challenges of anomaly detection in smart-factory manufacturing environments. Ma...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/13/1/21 |
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| author | Tae-yong Kim Jieun Lee Seokhyun Gong Jaehoon Lim Dowan Kim Jongpil Jeong |
| author_facet | Tae-yong Kim Jieun Lee Seokhyun Gong Jaehoon Lim Dowan Kim Jongpil Jeong |
| author_sort | Tae-yong Kim |
| collection | DOAJ |
| description | In this study, we present the few-shot generative adversarial network (FS-GAN) model, which integrates few-shot learning and a generative adversarial network with an unsupervised learning approach (AnoGAN) to address the challenges of anomaly detection in smart-factory manufacturing environments. Manufacturing processes often encounter malfunctions or defective parts that disrupt production and compromise product quality. However, collecting and labeling sufficient data to detect anomalies is time-intensive, and abnormal data are rare, leading to data imbalances. The FS-GAN model leverages few-shot learning to enable accurate predictions with minimal data and uses the generative capabilities of AnoGAN to mitigate the scarcity of abnormal data by generating synthetic normal data. Experimental results demonstrate that FS-GAN outperforms existing models in terms of accuracy and learning speed, even with limited datasets, effectively addressing the data imbalance problem inherent in manufacturing. The model reduces dependency on extensive data collection and labeling efforts, making it suitable for real-world applications. Through reliable and efficient anomaly detection, FS-GAN contributes to production reliability, product quality, and operational efficiency in smart factories. This study highlights the potential of FS-GAN to provide a cost-effective and high-performance solution to the challenges of anomaly detection in the manufacturing industry. |
| format | Article |
| id | doaj-art-2ff720e286ae48f18e4748a76564c512 |
| institution | Kabale University |
| issn | 2075-1702 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-2ff720e286ae48f18e4748a76564c5122025-01-24T13:39:10ZengMDPI AGMachines2075-17022024-12-011312110.3390/machines13010021A Novel FS-GAN-Based Anomaly Detection Approach for Smart ManufacturingTae-yong Kim0Jieun Lee1Seokhyun Gong2Jaehoon Lim3Dowan Kim4Jongpil Jeong5Department of Smart Factory Convergence, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of KoreaDepartment of Smart Factory Convergence, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of KoreaDepartment of Smart Factory Convergence, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of KoreaDepartment of Smart Factory Convergence, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of KoreaDepartment of Smart Factory Convergence, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of KoreaDepartment of Smart Factory Convergence, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of KoreaIn this study, we present the few-shot generative adversarial network (FS-GAN) model, which integrates few-shot learning and a generative adversarial network with an unsupervised learning approach (AnoGAN) to address the challenges of anomaly detection in smart-factory manufacturing environments. Manufacturing processes often encounter malfunctions or defective parts that disrupt production and compromise product quality. However, collecting and labeling sufficient data to detect anomalies is time-intensive, and abnormal data are rare, leading to data imbalances. The FS-GAN model leverages few-shot learning to enable accurate predictions with minimal data and uses the generative capabilities of AnoGAN to mitigate the scarcity of abnormal data by generating synthetic normal data. Experimental results demonstrate that FS-GAN outperforms existing models in terms of accuracy and learning speed, even with limited datasets, effectively addressing the data imbalance problem inherent in manufacturing. The model reduces dependency on extensive data collection and labeling efforts, making it suitable for real-world applications. Through reliable and efficient anomaly detection, FS-GAN contributes to production reliability, product quality, and operational efficiency in smart factories. This study highlights the potential of FS-GAN to provide a cost-effective and high-performance solution to the challenges of anomaly detection in the manufacturing industry.https://www.mdpi.com/2075-1702/13/1/21machine visiondeep learningfew-shot learningGANanomaly detection |
| spellingShingle | Tae-yong Kim Jieun Lee Seokhyun Gong Jaehoon Lim Dowan Kim Jongpil Jeong A Novel FS-GAN-Based Anomaly Detection Approach for Smart Manufacturing Machines machine vision deep learning few-shot learning GAN anomaly detection |
| title | A Novel FS-GAN-Based Anomaly Detection Approach for Smart Manufacturing |
| title_full | A Novel FS-GAN-Based Anomaly Detection Approach for Smart Manufacturing |
| title_fullStr | A Novel FS-GAN-Based Anomaly Detection Approach for Smart Manufacturing |
| title_full_unstemmed | A Novel FS-GAN-Based Anomaly Detection Approach for Smart Manufacturing |
| title_short | A Novel FS-GAN-Based Anomaly Detection Approach for Smart Manufacturing |
| title_sort | novel fs gan based anomaly detection approach for smart manufacturing |
| topic | machine vision deep learning few-shot learning GAN anomaly detection |
| url | https://www.mdpi.com/2075-1702/13/1/21 |
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