03967nam 2201081z- 450 991055749150332120231214132842.0(CKB)5400000000042916(oapen)https://directory.doabooks.org/handle/20.500.12854/76899(EXLCZ)99540000000004291620202201d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierAdvanced Process Monitoring for Industry 4.0Basel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 electronic resource (288 p.)3-0365-2073-2 3-0365-2074-0 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 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.Technology: general issuesbicsscspatial-temporal datapasting processprocess imageconvolutional neural networkIndustry 4.0auto machine learningfailure mode effects analysisrisk priority numberrolling bearingcondition monitoringclassificationOPTICSstatistical process controlcontrol chart patterndisruptionsdisruption managementfault diagnosisconstruction industryplaster productionneural networksdecision support systemsexpert systemsfailure mode and effects analysis (FMEA)discriminant analysisnon-intrusive load monitoringload identificationmembranedata reconciliationreal-timeonlinemonitoringSix Sigmamultivariate data analysislatent variables modelsPCAPLShigh-dimensional datastatistical process monitoringartificial generation of variabilitydata augmentationquality predictioncontinuous castingmultiscaletime series classificationimbalanced datacombustionoptical sensorsspectroscopy measurementssignal detectiondigital processingprincipal component analysiscurve resolutiondata miningsemiconductor manufacturingquality controlyield improvementfault detectionprocess controlmulti-phase residual recursive modelmulti-mode modelprocess monitoringTechnology: general issuesReis Marco Sedt1325356Gao FurongedtReis Marco SothGao FurongothBOOK9910557491503321Advanced Process Monitoring for Industry 4.03036793UNINA