A Case Driven Study of the Use of Time Series Classification for Flexibility in Industry 4.0
With the Industry 4.0 paradigm comes the convergence of the Internet Technologies and Operational Technologies, and concepts, such as Industrial Internet of Things (IIoT), cloud manufacturing, Cyber-Physical Systems (CPS), and so on. These concepts bring industries into the big data era and allow fo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/24/7273 |
| _version_ | 1797544171046699008 |
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| author | Julien Polge Jérémy Robert Yves Le Traon |
| author_facet | Julien Polge Jérémy Robert Yves Le Traon |
| author_sort | Julien Polge |
| collection | DOAJ |
| description | With the Industry 4.0 paradigm comes the convergence of the Internet Technologies and Operational Technologies, and concepts, such as Industrial Internet of Things (IIoT), cloud manufacturing, Cyber-Physical Systems (CPS), and so on. These concepts bring industries into the big data era and allow for them to have access to potentially useful information in order to optimise the Overall Equipment Effectiveness (OEE); however, most European industries still rely on the Computer-Integrated Manufacturing (CIM) model, where the production systems run as independent systems (i.e., without any communication with the upper levels). Those production systems are controlled by a Programmable Logic Controller, in which a static and rigid program is implemented. This program is static and rigid in a sense that the programmed routines cannot evolve over the time unless a human modifies it. However, to go further in terms of flexibility, we are convinced that it requires moving away from the aforementioned old-fashioned and rigid automation to a ML-based automation, i.e., where the control itself is based on the decisions that were taken by ML algorithms. In order to verify this, we applied a time series classification method on a scale model of a factory using real industrial controllers, and widened the variety of parts the production line has to treat. This study shows that satisfactory results can be obtained only at the expense of the human expertise (i.e., in the industrial process and in the ML process). |
| first_indexed | 2024-03-10T13:56:38Z |
| format | Article |
| id | doaj.art-e09b9a6387c3454691dfa687fa3e3441 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T13:56:38Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e09b9a6387c3454691dfa687fa3e34412023-11-21T01:35:00ZengMDPI AGSensors1424-82202020-12-012024727310.3390/s20247273A Case Driven Study of the Use of Time Series Classification for Flexibility in Industry 4.0Julien Polge0Jérémy Robert1Yves Le Traon2Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, 6 Rue Richard Coudenhove-Kalergi, L-1359 Luxembourg, LuxembourgInterdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, 6 Rue Richard Coudenhove-Kalergi, L-1359 Luxembourg, LuxembourgInterdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, 6 Rue Richard Coudenhove-Kalergi, L-1359 Luxembourg, LuxembourgWith the Industry 4.0 paradigm comes the convergence of the Internet Technologies and Operational Technologies, and concepts, such as Industrial Internet of Things (IIoT), cloud manufacturing, Cyber-Physical Systems (CPS), and so on. These concepts bring industries into the big data era and allow for them to have access to potentially useful information in order to optimise the Overall Equipment Effectiveness (OEE); however, most European industries still rely on the Computer-Integrated Manufacturing (CIM) model, where the production systems run as independent systems (i.e., without any communication with the upper levels). Those production systems are controlled by a Programmable Logic Controller, in which a static and rigid program is implemented. This program is static and rigid in a sense that the programmed routines cannot evolve over the time unless a human modifies it. However, to go further in terms of flexibility, we are convinced that it requires moving away from the aforementioned old-fashioned and rigid automation to a ML-based automation, i.e., where the control itself is based on the decisions that were taken by ML algorithms. In order to verify this, we applied a time series classification method on a scale model of a factory using real industrial controllers, and widened the variety of parts the production line has to treat. This study shows that satisfactory results can be obtained only at the expense of the human expertise (i.e., in the industrial process and in the ML process).https://www.mdpi.com/1424-8220/20/24/7273automation architectureflexibilityIndustry 4.0machine learningtime series classification |
| spellingShingle | Julien Polge Jérémy Robert Yves Le Traon A Case Driven Study of the Use of Time Series Classification for Flexibility in Industry 4.0 Sensors automation architecture flexibility Industry 4.0 machine learning time series classification |
| title | A Case Driven Study of the Use of Time Series Classification for Flexibility in Industry 4.0 |
| title_full | A Case Driven Study of the Use of Time Series Classification for Flexibility in Industry 4.0 |
| title_fullStr | A Case Driven Study of the Use of Time Series Classification for Flexibility in Industry 4.0 |
| title_full_unstemmed | A Case Driven Study of the Use of Time Series Classification for Flexibility in Industry 4.0 |
| title_short | A Case Driven Study of the Use of Time Series Classification for Flexibility in Industry 4.0 |
| title_sort | case driven study of the use of time series classification for flexibility in industry 4 0 |
| topic | automation architecture flexibility Industry 4.0 machine learning time series classification |
| url | https://www.mdpi.com/1424-8220/20/24/7273 |
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