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.
Subject
General Earth and Planetary Sciences,General Engineering,General Environmental Science
Reference25 articles.
1. Bergweiler S.: Intelligent Manufacturing based on Self-Monitoring Cyber-Physical Systems. UBICOMM 2015 The Ninth International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies, 2015.
2. Chen B., Abascal J., Soleimani M.: Electrical Resistance Tomography for Visualization of Moving Objects Using a Spatiotemporal Total Variation Regularization Algorithm. Sensors 18/2018, 1704.
3. Cox D., Snell E.: Ageneral definition of residuals. Journal of the Royal Statistical Society Series B (Methodological) 30/1968, 248–275.
4. Deszyńska A.: Modele hazardów proporcjonalnych Coxa. Matematyka stosowana 13(54)/2011.
5. Dušek J., Hladký D., Mikulka J.: Electrical Impedance Tomography Methods and Algorithms Processed with a GPU. PIERS Proceedings 2017, 1710–1714.
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