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|a books978-3-0365-2074-2
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|a 9783036520742
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|a 9783036520735
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|a Reis, Marco S.
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|a Advanced Process Monitoring for Industry 4.0
|h Elektronische Ressource
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| 260 |
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|a Basel, Switzerland
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2021
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| 300 |
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|a 1 electronic resource (288 p.)
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| 653 |
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|a quality control
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| 653 |
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|a process monitoring
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| 653 |
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|a risk priority number
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| 653 |
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|a data mining
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| 653 |
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|a optical sensors
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| 653 |
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|a Industry 4.0
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| 653 |
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|a signal detection
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| 653 |
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|a condition monitoring
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| 653 |
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|a rolling bearing
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| 653 |
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|a n/a
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| 653 |
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|a data reconciliation
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| 653 |
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|a data augmentation
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| 653 |
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|a multiscale
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| 653 |
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|a latent variables models
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| 653 |
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|a monitoring
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| 653 |
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|a pasting process
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| 653 |
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|a digital processing
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| 653 |
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|a auto machine learning
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| 653 |
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|a neural networks
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| 653 |
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|a online
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| 653 |
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|a process image
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| 653 |
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|a PCA
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| 653 |
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|a spatial-temporal data
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| 653 |
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|a disruption management
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| 653 |
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|a Six Sigma
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| 653 |
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|a fault detection
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| 653 |
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|a Technology: general issues / bicssc
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| 653 |
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|a decision support systems
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| 653 |
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|a plaster production
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| 653 |
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|a imbalanced data
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| 653 |
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|a high-dimensional data
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| 653 |
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|a curve resolution
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| 653 |
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|a multivariate data analysis
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| 653 |
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|a failure mode and effects analysis (FMEA)
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| 653 |
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|a PLS
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| 653 |
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|a principal component analysis
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| 653 |
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|a non-intrusive load monitoring
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| 653 |
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|a continuous casting
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| 653 |
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|a semiconductor manufacturing
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| 653 |
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|a real-time
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| 653 |
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|a OPTICS
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| 653 |
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|a process control
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| 653 |
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|a statistical process monitoring
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| 653 |
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|a time series classification
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| 653 |
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|a yield improvement
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| 653 |
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|a expert systems
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| 653 |
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|a spectroscopy measurements
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| 653 |
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|a discriminant analysis
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| 653 |
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|a fault diagnosis
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| 653 |
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|a control chart pattern
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| 653 |
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|a statistical process control
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| 653 |
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|a combustion
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| 653 |
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|a failure mode effects analysis
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| 653 |
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|a artificial generation of variability
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| 653 |
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|a disruptions
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| 653 |
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|a quality prediction
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| 653 |
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|a membrane
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| 653 |
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|a classification
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| 653 |
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|a multi-phase residual recursive model
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| 653 |
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|a construction industry
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| 653 |
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|a multi-mode model
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| 653 |
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|a convolutional neural network
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| 653 |
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|a load identification
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| 700 |
1 |
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|a Gao, Furong
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| 700 |
1 |
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|a Reis, Marco S.
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| 700 |
1 |
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|a Gao, Furong
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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| 028 |
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|a 10.3390/books978-3-0365-2074-2
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|u https://www.mdpi.com/books/pdfview/book/4369
|7 0
|x Verlag
|3 Volltext
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| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/76899
|z DOAB: description of the publication
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|a 700
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|a 600
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|a 330
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|a 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.
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