000			04157naaaa2201057uu 4500
001			https://directory.doabooks.org/handle/20.500.12854/76899
005			20220714185521.0
020			_abooks978-3-0365-2074-2
020			_a9783036520735
020			_a9783036520742
024	7		_a10.3390/books978-3-0365-2074-2 _cdoi
041	0		_aEnglish
042			_adc
072		7	_aTB _2bicssc
100	1		_aReis, Marco S. _4edt _91608424
700	1		_aGao, Furong _4edt _91608425
700	1		_aReis, Marco S. _4oth _91608424
700	1		_aGao, Furong _4oth _91608425
245	1	0	_aAdvanced Process Monitoring for Industry 4.0
260			_aBasel, Switzerland _bMDPI - Multidisciplinary Digital Publishing Institute _c2021
300			_a1 electronic resource (288 p.)
506	0		_aOpen Access _2star _fUnrestricted online access
520			_aThis 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.
540			_aCreative Commons _fhttps://creativecommons.org/licenses/by/4.0/ _2cc _4https://creativecommons.org/licenses/by/4.0/
546			_aEnglish
650		7	_aTechnology: general issues _2bicssc _9928609
653			_aspatial-temporal data
653			_apasting process
653			_aprocess image
653			_aconvolutional neural network
653			_aIndustry 4.0
653			_aauto machine learning
653			_afailure mode effects analysis
653			_arisk priority number
653			_arolling bearing
653			_acondition monitoring
653			_aclassification
653			_aOPTICS
653			_astatistical process control
653			_acontrol chart pattern
653			_adisruptions
653			_adisruption management
653			_afault diagnosis
653			_aconstruction industry
653			_aplaster production
653			_aneural networks
653			_adecision support systems
653			_aexpert systems
653			_afailure mode and effects analysis (FMEA)
653			_adiscriminant analysis
653			_anon-intrusive load monitoring
653			_aload identification
653			_amembrane
653			_adata reconciliation
653			_areal-time
653			_aonline
653			_amonitoring
653			_aSix Sigma
653			_amultivariate data analysis
653			_alatent variables models
653			_aPCA
653			_aPLS
653			_ahigh-dimensional data
653			_astatistical process monitoring
653			_aartificial generation of variability
653			_adata augmentation
653			_aquality prediction
653			_acontinuous casting
653			_amultiscale
653			_atime series classification
653			_aimbalanced data
653			_acombustion
653			_aoptical sensors
653			_aspectroscopy measurements
653			_asignal detection
653			_adigital processing
653			_aprincipal component analysis
653			_acurve resolution
653			_adata mining
653			_asemiconductor manufacturing
653			_aquality control
653			_ayield improvement
653			_afault detection
653			_aprocess control
653			_amulti-phase residual recursive model
653			_amulti-mode model
653			_aprocess monitoring
653			_an/a
856	4	0	_awww.oapen.org _uhttps://mdpi.com/books/pdfview/book/4369 _70 _zDOAB: download the publication
856	4	0	_awww.oapen.org _uhttps://directory.doabooks.org/handle/20.500.12854/76899 _70 _zDOAB: description of the publication
999			_c3008534 _d3008534