TY - BOOK ID - 133881997 TI - Advanced Process Monitoring for Industry 4.0 AU - Reis, Marco S. AU - Gao, Furong PY - 2021 PB - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute DB - UniCat KW - Technology: general issues KW - spatial-temporal data KW - pasting process KW - process image KW - convolutional neural network KW - Industry 4.0 KW - auto machine learning KW - failure mode effects analysis KW - risk priority number KW - rolling bearing KW - condition monitoring KW - classification KW - OPTICS KW - statistical process control KW - control chart pattern KW - disruptions KW - disruption management KW - fault diagnosis KW - construction industry KW - plaster production KW - neural networks KW - decision support systems KW - expert systems KW - failure mode and effects analysis (FMEA) KW - discriminant analysis KW - non-intrusive load monitoring KW - load identification KW - membrane KW - data reconciliation KW - real-time KW - online KW - monitoring KW - Six Sigma KW - multivariate data analysis KW - latent variables models KW - PCA KW - PLS KW - high-dimensional data KW - statistical process monitoring KW - artificial generation of variability KW - data augmentation KW - quality prediction KW - continuous casting KW - multiscale KW - time series classification KW - imbalanced data KW - combustion KW - optical sensors KW - spectroscopy measurements KW - signal detection KW - digital processing KW - principal component analysis KW - curve resolution KW - data mining KW - semiconductor manufacturing KW - quality control KW - yield improvement KW - fault detection KW - process control KW - multi-phase residual recursive model KW - multi-mode model KW - process monitoring KW - n/a UR - https://www.unicat.be/uniCat?func=search&query=sysid:133881997 AB - 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. ER -