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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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Guardat en:
Format: Online
Idioma:anglès
Publicat: MDPI - Multidisciplinary Digital Publishing Institute 2022
Matèries:
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
n/a
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues
Accés en línia:ONIX_20220111_9783036520735_634
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Sumari: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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