Abstract
Modern production systems rely heavily on data from the machines, processes and other sources. The amount and resolution of the data increases continuously allowing complex models like Machine Learning models to be trained and applied in production. All analytics and models that rely on the production data implicitly require a suitable data quality. Central level of data generation and acquisition is the machine level where data from machine and sensors are generated and need to be aggregated. In this paper, we define the Time Synchronization Problem that arise from the different data sources and technical properties of computerized systems in manufacturing directly impacting the quality of Time-Series data. Further, we cluster current approaches and discuss their applicability in data-intense manufacturing.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
