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dc.contributor.editorReis, Marco S.
dc.contributor.editorGao, Furong
dc.date.accessioned2022-01-11T13:45:36Z
dc.date.available2022-01-11T13:45:36Z
dc.date.issued2021
dc.identifierONIX_20220111_9783036520735_634
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/76899
dc.description.abstractThis 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.
dc.languageEnglish
dc.subject.classificationthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issuesen_US
dc.subject.otherspatial-temporal data
dc.subject.otherpasting process
dc.subject.otherprocess image
dc.subject.otherconvolutional neural network
dc.subject.otherIndustry 4.0
dc.subject.otherauto machine learning
dc.subject.otherfailure mode effects analysis
dc.subject.otherrisk priority number
dc.subject.otherrolling bearing
dc.subject.othercondition monitoring
dc.subject.otherclassification
dc.subject.otherOPTICS
dc.subject.otherstatistical process control
dc.subject.othercontrol chart pattern
dc.subject.otherdisruptions
dc.subject.otherdisruption management
dc.subject.otherfault diagnosis
dc.subject.otherconstruction industry
dc.subject.otherplaster production
dc.subject.otherneural networks
dc.subject.otherdecision support systems
dc.subject.otherexpert systems
dc.subject.otherfailure mode and effects analysis (FMEA)
dc.subject.otherdiscriminant analysis
dc.subject.othernon-intrusive load monitoring
dc.subject.otherload identification
dc.subject.othermembrane
dc.subject.otherdata reconciliation
dc.subject.otherreal-time
dc.subject.otheronline
dc.subject.othermonitoring
dc.subject.otherSix Sigma
dc.subject.othermultivariate data analysis
dc.subject.otherlatent variables models
dc.subject.otherPCA
dc.subject.otherPLS
dc.subject.otherhigh-dimensional data
dc.subject.otherstatistical process monitoring
dc.subject.otherartificial generation of variability
dc.subject.otherdata augmentation
dc.subject.otherquality prediction
dc.subject.othercontinuous casting
dc.subject.othermultiscale
dc.subject.othertime series classification
dc.subject.otherimbalanced data
dc.subject.othercombustion
dc.subject.otheroptical sensors
dc.subject.otherspectroscopy measurements
dc.subject.othersignal detection
dc.subject.otherdigital processing
dc.subject.otherprincipal component analysis
dc.subject.othercurve resolution
dc.subject.otherdata mining
dc.subject.othersemiconductor manufacturing
dc.subject.otherquality control
dc.subject.otheryield improvement
dc.subject.otherfault detection
dc.subject.otherprocess control
dc.subject.othermulti-phase residual recursive model
dc.subject.othermulti-mode model
dc.subject.otherprocess monitoring
dc.subject.othern/a
dc.titleAdvanced Process Monitoring for Industry 4.0
dc.typebook
oapen.identifier.doi10.3390/books978-3-0365-2074-2
oapen.relation.isPublishedBy46cabcaa-dd94-4bfe-87b4-55023c1b36d0
oapen.relation.isbn9783036520735
oapen.relation.isbn9783036520742
oapen.pages288
oapen.place.publicationBasel, Switzerland


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