iconicon Uproszczony widok
 

ANALYSIS OF DATA FROM MEASURING SENSORS FOR PREDICTION IN PRODUCTION PROCESS CONTROL SYSTEMS

cris.lastimport.scopus2024-09-19T01:30:24Z
dc.abstract.enThe article presents a solution based on a cyber-physical system in which data collected from measuring sensors was analysed for prediction in the production process control system. The presented technology was based on intelligent sensors as part of the solution for Industry 4.0. The main purpose of the work is to reduce data and select the appropriate covariate to optimise modelling of defects using the Cox model for a specific mechanical system. The reliability of machines and devices in the production process is a condition for ensuring continuity of production. Predicting damage, especially its movement, gives the ability to monitor the current state of the machine. In a broader perspective, this enables streamlining the production process, service planning or control. This ensures production continuity and optimal performance. The presented model is a regressive survival analysis model that allows you to calculate the probability of failure occurring over a given period of time.
dc.affiliationTransportu i Informatyki
dc.contributor.authorTomasz Rymarczyk
dc.contributor.authorBartek Przysucha
dc.contributor.authorMarcin Kowalski
dc.contributor.authorPiotr Bednarczuk
dc.date.accessioned2024-05-10T08:12:44Z
dc.date.available2024-05-10T08:12:44Z
dc.date.issued2019
dc.identifier.doi10.35784/iapgos.570
dc.identifier.issn2391-6761
dc.identifier.issn2083-0157
dc.identifier.urihttps://repo.akademiawsei.eu/handle/item/316
dc.languageen
dc.pbn.affiliationinformation and communication technology
dc.relation.ispartofInformatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
dc.rightsCC-BY-SA
dc.subject.enCox model
dc.subject.entime to failure prediction
dc.subject.enproduction control
dc.subject.enintelligent platform
dc.titleANALYSIS OF DATA FROM MEASURING SENSORS FOR PREDICTION IN PRODUCTION PROCESS CONTROL SYSTEMS
dc.typeReviewArticle
dspace.entity.typePublication
oaire.citation.issue4
oaire.citation.volume9