Data-driven fault detection and positioning of eccentric rolls in roll-to-roll systems using wrap angle and sensor proximity
This paper presents a novel approach for detecting and positioning eccentricity defects in roll-to-roll (R2R) slot-die coating systems, which is critical for maintaining high production quality and efficiency. It introduces the feature combination matrix (FCM) method, which improves fault detection...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024018723 |
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| _version_ | 1850122882570518528 |
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| author | Yoonjae Lee Minjae Kim Jaehyun Noh Gyoujin Cho Changwoo Lee |
| author_facet | Yoonjae Lee Minjae Kim Jaehyun Noh Gyoujin Cho Changwoo Lee |
| author_sort | Yoonjae Lee |
| collection | DOAJ |
| description | This paper presents a novel approach for detecting and positioning eccentricity defects in roll-to-roll (R2R) slot-die coating systems, which is critical for maintaining high production quality and efficiency. It introduces the feature combination matrix (FCM) method, which improves fault detection and precise positioning of eccentric rolls through focused feature engineering. The study employs the FCM method to enhance defect detection accuracy, using Support Vector Machine (SVM) as the classifier to consistently evaluate the effectiveness of selected feature sets in identifying and positioning eccentricity in R2R systems. By leveraging tension data, optimal feature sets are identified, emphasizing the Mean, Fast Fourier Transform, and other key variables that capture crucial system dynamics, including wrap angles and sensor proximities. Classification algorithms were used to compare the performance of models employing these optimized FCM-based features against traditional models utilizing a full suite of 40 statistical features. The FCM methodology achieved a notable improvement in eccentric roll detection and positioning accuracy, reaching 97.3 %, while also reducing data capacity requirements and processing time. These outcomes highlight the effectiveness of selective feature optimization and strategic combinations, demonstrating that high-impact features enhance both detection accuracy and efficiency. Conclusively, the FCM is positioned as a pivotal tool for advancing industrial diagnostic processes, with future work suggested in the areas of adaptive feature extraction techniques and real-time model integration to improve the reliability and adaptability of R2R manufacturing. |
| format | Article |
| id | doaj-art-dc619a5f195f4dffbd01c0cf877ba2e4 |
| institution | OA Journals |
| issn | 2590-1230 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-dc619a5f195f4dffbd01c0cf877ba2e42025-08-20T02:34:43ZengElsevierResults in Engineering2590-12302024-12-012410362910.1016/j.rineng.2024.103629Data-driven fault detection and positioning of eccentric rolls in roll-to-roll systems using wrap angle and sensor proximityYoonjae Lee0Minjae Kim1Jaehyun Noh2Gyoujin Cho3Changwoo Lee4Department of Mechanical Design and Production Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, South KoreaDepartment of Mechanical Design and Production Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, South KoreaDepartment of Mechanical Design and Production Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, South KoreaDepartment Institute of Quantum Biophysics, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon 16419, South KoreaDepartment of Mechanical Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, South Korea; Corresponding author.This paper presents a novel approach for detecting and positioning eccentricity defects in roll-to-roll (R2R) slot-die coating systems, which is critical for maintaining high production quality and efficiency. It introduces the feature combination matrix (FCM) method, which improves fault detection and precise positioning of eccentric rolls through focused feature engineering. The study employs the FCM method to enhance defect detection accuracy, using Support Vector Machine (SVM) as the classifier to consistently evaluate the effectiveness of selected feature sets in identifying and positioning eccentricity in R2R systems. By leveraging tension data, optimal feature sets are identified, emphasizing the Mean, Fast Fourier Transform, and other key variables that capture crucial system dynamics, including wrap angles and sensor proximities. Classification algorithms were used to compare the performance of models employing these optimized FCM-based features against traditional models utilizing a full suite of 40 statistical features. The FCM methodology achieved a notable improvement in eccentric roll detection and positioning accuracy, reaching 97.3 %, while also reducing data capacity requirements and processing time. These outcomes highlight the effectiveness of selective feature optimization and strategic combinations, demonstrating that high-impact features enhance both detection accuracy and efficiency. Conclusively, the FCM is positioned as a pivotal tool for advancing industrial diagnostic processes, with future work suggested in the areas of adaptive feature extraction techniques and real-time model integration to improve the reliability and adaptability of R2R manufacturing.http://www.sciencedirect.com/science/article/pii/S2590123024018723Eccentricity positioningFault detectionSensor proximityWeb handlingWrap angle |
| spellingShingle | Yoonjae Lee Minjae Kim Jaehyun Noh Gyoujin Cho Changwoo Lee Data-driven fault detection and positioning of eccentric rolls in roll-to-roll systems using wrap angle and sensor proximity Results in Engineering Eccentricity positioning Fault detection Sensor proximity Web handling Wrap angle |
| title | Data-driven fault detection and positioning of eccentric rolls in roll-to-roll systems using wrap angle and sensor proximity |
| title_full | Data-driven fault detection and positioning of eccentric rolls in roll-to-roll systems using wrap angle and sensor proximity |
| title_fullStr | Data-driven fault detection and positioning of eccentric rolls in roll-to-roll systems using wrap angle and sensor proximity |
| title_full_unstemmed | Data-driven fault detection and positioning of eccentric rolls in roll-to-roll systems using wrap angle and sensor proximity |
| title_short | Data-driven fault detection and positioning of eccentric rolls in roll-to-roll systems using wrap angle and sensor proximity |
| title_sort | data driven fault detection and positioning of eccentric rolls in roll to roll systems using wrap angle and sensor proximity |
| topic | Eccentricity positioning Fault detection Sensor proximity Web handling Wrap angle |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024018723 |
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