Open AccessTroubleshooting technological aggregates based on machine learning
Author(s) -
S. A. Kosarevskaia,
А. В. Шукалов,
И. О. Жаринов,
О. О. Жаринов
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1047/1/012050
Subject(s) - troubleshooting , preventive maintenance , factory (object oriented programming) , reliability (semiconductor) , production (economics) , cyber physical system , reliability engineering , function (biology) , control (management) , engineering , predictive maintenance , computer science , process (computing) , manufacturing engineering , artificial intelligence , operating system , power (physics) , physics , quantum mechanics , evolutionary biology , biology , economics , macroeconomics , programming language
The technological aggregates preventive maintenance problem being researched is resistive against a type of failures. The preventive maintenance is a cyber-production control system function and is done by activation of re-arrangement manipulation system, which is used to eliminate technological aggregates failures. The technological aggregates restoration operation technology is viewed as a man substitution in the Industry 4.0 paradigm. The equipment failure is viewed as an incident of cyber-production functional safety system, which is capable to cause a potential complex damage. The equipment robotized maintenance model is oriented to increase the cyber-production functional reliability and to use the machine learning methods to make intellectual the industrial automatics. They analyze the repair and restoration works mechanisms in the process factory level (the physical workshop) and in the analytical factory level (the virtual cloud) for a cyber-production, which interact with operation system. There is the equipment robotized maintenance algorithm proposed, which gives the data to the control system for its actual state. There is the automatic control system scheme proposed to make a technological aggregates preventive maintenance. An option how to change a failed unit is selected after the technological aggregates conditions pre-history analyzing obtained in the stage of machine learning of collected statistics.