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Advanced Process Monitoring for Industry 4.0

Edited by
September 2021
288 pages
  • ISBN978-3-0365-2073-5 (Hardback)
  • ISBN978-3-0365-2074-2 (PDF)
https://doi.org/10.3390/books978-3-0365-2074-2
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This book is a reprint of the Special Issue Advanced Process Monitoring for Industry 4.0 that was published in

Biology & Life Sciences
Chemistry & Materials Science
Computer Science & Mathematics
Engineering
Environmental & Earth Sciences
Summary

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.

Format
  • Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
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; Industry 4.0; 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; convolutional neural network; membrane; data reconciliation; real-time; online; monitoring; Six Sigma; Industry 4.0; multivariate data analysis; latent variables models; PCA; PLS; high-dimensional data; statistical process monitoring; artificial generation of variability; data augmentation; Industry 4.0; quality prediction; continuous casting; multiscale; convolutional neural network; time series classification; imbalanced data; combustion; optical sensors; spectroscopy measurements; signal detection; digital processing; principal component analysis; multivariate data analysis; curve resolution; data mining; semiconductor manufacturing; quality control; yield improvement; fault detection; process control; multi-phase residual recursive model; multi-mode model; quality prediction; process monitoring; n/a