TY  - GEN
AU  - Reis,Marco S.
AU  - Gao,Furong
AU  - Reis,Marco S.
AU  - Gao,Furong
TI  - Advanced Process Monitoring for Industry 4.0
SN  - books978-3-0365-2074-2
PY  - 2021///
CY  - Basel, Switzerland
PB  - MDPI - Multidisciplinary Digital Publishing Institute
KW  - Technology: general issues
KW  - bicssc
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
N1  - Open Access
N2  - 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
UR  - https://mdpi.com/books/pdfview/book/4369
UR  - https://directory.doabooks.org/handle/20.500.12854/76899
ER  -