| Title | An Automatic Process Monitoring Method Using Recurrence Plot in Progressive Stamping Processes |
| Authors | Zhou, Cheng Liu, Kaibo Zhang, Xi Zhang, Weidong Shi, Jianjun |
| Affiliation | Univ Sci & Technol Beijing, Natl Ctr Mat Serv Safety, Beijing 100083, Peoples R China. Univ Wisconsin, Dept Ind & Syst Engn, Madison, WI 53706 USA. Peking Univ, Dept Ind Engn & Management, Beijing 100871, Peoples R China. Georgia Inst Technol, H Milton Stewart Sch Ind & Syst Engn, Atlanta, GA 30332 USA. |
| Keywords | Process monitoring progressive stamping processes recurrence plot (RP) tonnage signals QUANTIFICATION ANALYSIS HAAR-TRANSFORM TIME-SERIES SYSTEMS THRESHOLD |
| Issue Date | 2016 |
| Publisher | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING |
| Citation | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING.2016,13,(2),1102-1111. |
| Abstract | In 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. |
| URI | http://hdl.handle.net/20.500.11897/437455 |
| ISSN | 1545-5955 |
| DOI | 10.1109/TASE.2015.2468058 |
| Indexed | SCI(E) EI |
| Appears in Collections: | 工学院 |
