Title :
Analytics of Industrial Operational Data Inspired by Natural Language Processing
Author_Institution :
NASK - Res. & Acad. Comput. Network, Warsaw, Poland
Abstract :
Industrial processes provide lots of operational data on different timescales. Those data are well-structured and used now for daily control, longer-term management and forensics. We propose to pre-process that data and treat them the way the natural language processing is done - first, in order to find common ways the process is controlled. Such knowledge can then be used in prediction or early detection of faults, or necessary manufacturing shifts. Gas transmission operational data are considered here as the live example.
Keywords :
manufacturing systems; natural language processing; petroleum industry; production engineering computing; daily control; gas transmission operational data; industrial operational data; longer-term management; manufacturing shifts; natural language processing; Big data; Data models; History; Mathematical model; Natural language processing; predictive analytics; smart historian;
Conference_Titel :
Big Data (BigData Congress), 2015 IEEE International Congress on
Conference_Location :
New York, NY
Print_ISBN :
978-1-4673-7277-0
DOI :
10.1109/BigDataCongress.2015.108
