Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/641066
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dc.contributor.authorCheng Zhouen_US
dc.contributor.authorKaibo Liuen_US
dc.contributor.authorXi Zhangen_US
dc.contributor.authorWeidong Zhangen_US
dc.contributor.authorJianjun Shien_US
dc.date.accessioned2020-05-20T10:03:33Z-
dc.date.available2020-05-20T10:03:33Z-
dc.date.issued2016en_US
dc.identifier.issn1545-5955en_US
dc.identifier.issn1558-3783en_US
dc.identifier.other10.1109/TASE.2015.2468058en_US
dc.identifier.urihttp://localhost/handle/Hannan/175286en_US
dc.identifier.urihttp://localhost/handle/Hannan/641066-
dc.description.abstractIn progressive stamping processes, condition monitoring based on tonnage signals is of great practical significance. One typical fault in progressive stamping processes is a missing part in one of the die stations due to malfunction of part transfer in the press. One challenging question is how to detect the fault due to the missing part in certain die stations as such a fault often results in die or press damage, but only provides a small change in the tonnage signals. To address this issue, this article proposes a novel automatic process monitoring method using the recurrence plot (RP) method. Along with the developed method, we also provide a detailed interpretation of the representative patterns in the recurrence plot. Then, the corresponding relationship between the RPs and the tonnage signals under different process conditions is fully investigated. To differentiate the tonnage signals under normal and faulty conditions, we adopt the recurrence quantification analysis (RQA) to characterize the critical patterns in the RPs. A parameter learning algorithm is developed to set up the appropriate parameter of the RP method for progressive stamping processes. A real case study is provided to validate our approach, and the results are compared with the existing literature to demonstrate the outperformance of this proposed monitoring method.en_US
dc.publisherIEEEen_US
dc.relation.haspart7239651.pdfen_US
dc.subjecttonnage signals|progressive stamping processes|recurrence plot (RP)|Process monitoringen_US
dc.titleAn Automatic Process Monitoring Method Using Recurrence Plot in Progressive Stamping Processesen_US
dc.typeArticleen_US
dc.journal.volume13en_US
dc.journal.issue2en_US
dc.journal.titleIEEE Transactions on Automation Science and Engineeringen_US
Appears in Collections:2016

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Full metadata record
DC FieldValueLanguage
dc.contributor.authorCheng Zhouen_US
dc.contributor.authorKaibo Liuen_US
dc.contributor.authorXi Zhangen_US
dc.contributor.authorWeidong Zhangen_US
dc.contributor.authorJianjun Shien_US
dc.date.accessioned2020-05-20T10:03:33Z-
dc.date.available2020-05-20T10:03:33Z-
dc.date.issued2016en_US
dc.identifier.issn1545-5955en_US
dc.identifier.issn1558-3783en_US
dc.identifier.other10.1109/TASE.2015.2468058en_US
dc.identifier.urihttp://localhost/handle/Hannan/175286en_US
dc.identifier.urihttp://localhost/handle/Hannan/641066-
dc.description.abstractIn progressive stamping processes, condition monitoring based on tonnage signals is of great practical significance. One typical fault in progressive stamping processes is a missing part in one of the die stations due to malfunction of part transfer in the press. One challenging question is how to detect the fault due to the missing part in certain die stations as such a fault often results in die or press damage, but only provides a small change in the tonnage signals. To address this issue, this article proposes a novel automatic process monitoring method using the recurrence plot (RP) method. Along with the developed method, we also provide a detailed interpretation of the representative patterns in the recurrence plot. Then, the corresponding relationship between the RPs and the tonnage signals under different process conditions is fully investigated. To differentiate the tonnage signals under normal and faulty conditions, we adopt the recurrence quantification analysis (RQA) to characterize the critical patterns in the RPs. A parameter learning algorithm is developed to set up the appropriate parameter of the RP method for progressive stamping processes. A real case study is provided to validate our approach, and the results are compared with the existing literature to demonstrate the outperformance of this proposed monitoring method.en_US
dc.publisherIEEEen_US
dc.relation.haspart7239651.pdfen_US
dc.subjecttonnage signals|progressive stamping processes|recurrence plot (RP)|Process monitoringen_US
dc.titleAn Automatic Process Monitoring Method Using Recurrence Plot in Progressive Stamping Processesen_US
dc.typeArticleen_US
dc.journal.volume13en_US
dc.journal.issue2en_US
dc.journal.titleIEEE Transactions on Automation Science and Engineeringen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7239651.pdf1.54 MBAdobe PDFThumbnail
Preview File
Full metadata record
DC FieldValueLanguage
dc.contributor.authorCheng Zhouen_US
dc.contributor.authorKaibo Liuen_US
dc.contributor.authorXi Zhangen_US
dc.contributor.authorWeidong Zhangen_US
dc.contributor.authorJianjun Shien_US
dc.date.accessioned2020-05-20T10:03:33Z-
dc.date.available2020-05-20T10:03:33Z-
dc.date.issued2016en_US
dc.identifier.issn1545-5955en_US
dc.identifier.issn1558-3783en_US
dc.identifier.other10.1109/TASE.2015.2468058en_US
dc.identifier.urihttp://localhost/handle/Hannan/175286en_US
dc.identifier.urihttp://localhost/handle/Hannan/641066-
dc.description.abstractIn progressive stamping processes, condition monitoring based on tonnage signals is of great practical significance. One typical fault in progressive stamping processes is a missing part in one of the die stations due to malfunction of part transfer in the press. One challenging question is how to detect the fault due to the missing part in certain die stations as such a fault often results in die or press damage, but only provides a small change in the tonnage signals. To address this issue, this article proposes a novel automatic process monitoring method using the recurrence plot (RP) method. Along with the developed method, we also provide a detailed interpretation of the representative patterns in the recurrence plot. Then, the corresponding relationship between the RPs and the tonnage signals under different process conditions is fully investigated. To differentiate the tonnage signals under normal and faulty conditions, we adopt the recurrence quantification analysis (RQA) to characterize the critical patterns in the RPs. A parameter learning algorithm is developed to set up the appropriate parameter of the RP method for progressive stamping processes. A real case study is provided to validate our approach, and the results are compared with the existing literature to demonstrate the outperformance of this proposed monitoring method.en_US
dc.publisherIEEEen_US
dc.relation.haspart7239651.pdfen_US
dc.subjecttonnage signals|progressive stamping processes|recurrence plot (RP)|Process monitoringen_US
dc.titleAn Automatic Process Monitoring Method Using Recurrence Plot in Progressive Stamping Processesen_US
dc.typeArticleen_US
dc.journal.volume13en_US
dc.journal.issue2en_US
dc.journal.titleIEEE Transactions on Automation Science and Engineeringen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7239651.pdf1.54 MBAdobe PDFThumbnail
Preview File