Advanced Process Monitoring for Industry 4.0
This book reports recent advances on Process Monitoring (PM) to cope with the many challenges raised by the new production systems, sensors and “extreme data” conditions that emerged with Industry 4.0. Concepts such as digital-twins and deep learning are brought to the PM arena, pushing forward the...
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| Year of Publication: | 2021 |
| Language: | English |
| Physical Description: | 1 electronic resource (288 p.) |
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(CKB)5400000000042916 (oapen)https://directory.doabooks.org/handle/20.500.12854/76899 (EXLCZ)995400000000042916 |
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Reis, Marco S. edt Advanced Process Monitoring for Industry 4.0 Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021 1 electronic resource (288 p.) text txt rdacontent computer c rdamedia online resource cr rdacarrier Open access Unrestricted online access star This book reports recent advances on Process Monitoring (PM) to cope with the many challenges raised by the new production systems, sensors and “extreme data” conditions that emerged with Industry 4.0. Concepts such as digital-twins and deep learning are brought to the PM arena, pushing forward the capabilities of existing methodologies to handle more complex scenarios. The evolution of classical paradigms such as Latent Variable modeling, Six Sigma and FMEA are also covered. Applications span a wide range of domains such as microelectronics, semiconductors, chemicals, materials, agriculture, as well as the monitoring of rotating equipment, combustion systems and membrane separation processes. English Technology: general issues bicssc spatial-temporal data pasting process process image convolutional neural network Industry 4.0 auto machine learning failure mode effects analysis risk priority number rolling bearing condition monitoring classification OPTICS statistical process control control chart pattern disruptions disruption management fault diagnosis construction industry plaster production neural networks decision support systems expert systems failure mode and effects analysis (FMEA) discriminant analysis non-intrusive load monitoring load identification membrane data reconciliation real-time online monitoring Six Sigma multivariate data analysis latent variables models PCA PLS high-dimensional data statistical process monitoring artificial generation of variability data augmentation quality prediction continuous casting multiscale time series classification imbalanced data combustion optical sensors spectroscopy measurements signal detection digital processing principal component analysis curve resolution data mining semiconductor manufacturing quality control yield improvement fault detection process control multi-phase residual recursive model multi-mode model process monitoring 3-0365-2073-2 3-0365-2074-0 Gao, Furong edt Reis, Marco S. oth Gao, Furong oth |
| language |
English |
| format |
eBook |
| author2 |
Gao, Furong Reis, Marco S. Gao, Furong |
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Gao, Furong Reis, Marco S. Gao, Furong |
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m s r ms msr f g fg |
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HerausgeberIn Sonstige Sonstige |
| title |
Advanced Process Monitoring for Industry 4.0 |
| spellingShingle |
Advanced Process Monitoring for Industry 4.0 |
| title_full |
Advanced Process Monitoring for Industry 4.0 |
| title_fullStr |
Advanced Process Monitoring for Industry 4.0 |
| title_full_unstemmed |
Advanced Process Monitoring for Industry 4.0 |
| title_auth |
Advanced Process Monitoring for Industry 4.0 |
| title_new |
Advanced Process Monitoring for Industry 4.0 |
| title_sort |
advanced process monitoring for industry 4.0 |
| publisher |
MDPI - Multidisciplinary Digital Publishing Institute |
| publishDate |
2021 |
| physical |
1 electronic resource (288 p.) |
| isbn |
3-0365-2073-2 3-0365-2074-0 |
| illustrated |
Not Illustrated |
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AT reismarcos advancedprocessmonitoringforindustry40 AT gaofurong advancedprocessmonitoringforindustry40 |
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(CKB)5400000000042916 (oapen)https://directory.doabooks.org/handle/20.500.12854/76899 (EXLCZ)995400000000042916 |
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Advanced Process Monitoring for Industry 4.0 |
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