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


Abstract

The 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.


Keywords

Cox Model; Time to Failure Prediction; Production Control; Intelligent Platform

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Published : 2019-12-15


Rymarczyk, T., Przysucha, B., Kowalski, M., & Bednarczuk, P. (2019). ANALYSIS OF DATA FROM MEASURING SENSORS FOR PREDICTION IN PRODUCTION PROCESS CONTROL SYSTEMS. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 9(4), 26-29. https://doi.org/10.35784/iapgos.570

Tomasz Rymarczyk  tomasz@rymarczyk.com
1. Research & Development Centre Netrix SA; 2. University of Economics and Innovation in Lublin  Poland
http://orcid.org/0000-0002-3524-9151
Bartek Przysucha 
Lublin University of Technology  Poland
http://orcid.org/0000-0002-1117-8088
Marcin Kowalski 
University of Economics and Innovation in Lublin  Poland
https://orcid.org/0000-0002-1644-0612
Piotr Bednarczuk 
University of Economics and Innovation in Lublin  Poland
https://orcid.org/0000-0003-1933-7183