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Advanced Process Monitoring for Industry 4.0
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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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Keywords
- artificial generation of variability
- auto machine learning
- Classification
- combustion
- condition monitoring
- Construction industry
- continuous casting
- control chart pattern
- convolutional neural network
- curve resolution
- data augmentation
- Data mining
- data reconciliation
- Decision support systems
- digital processing
- Discriminant analysis
- disruption management
- Disruptions
- expert systems
- failure mode and effects analysis (FMEA)
- failure mode effects analysis
- fault detection
- fault diagnosis
- High-dimensional data
- imbalanced data
- Industry 4.0
- latent variables models
- load identification
- membrane
- Monitoring
- multi-mode model
- multi-phase residual recursive model
- multiscale
- multivariate data analysis
- n/a
- neural networks
- non-intrusive load monitoring
- Online
- optical sensors
- Optics
- pasting process
- PCA
- plaster production
- PLS
- principal component analysis
- Process control
- process image
- process monitoring
- Quality control
- quality prediction
- Real-time
- risk priority number
- rolling bearing
- semiconductor manufacturing
- Signal detection
- six sigma
- spatial-temporal data
- spectroscopy measurements
- statistical process control
- statistical process monitoring
- Technology, engineering, agriculture
- Technology: general issues
- time series classification
- yield improvement
Links
DOI: 10.3390/books978-3-0365-2074-2Editions