Open AccessReliability indicators analysis of industrial enterprises products by using neural networks
Author(s) -
Ya I. Shamlitskiy,
S. N. Mironenko,
Anton Devyatkov,
N. V. Bezrukova
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1582/1/012076
Subject(s) - reliability (semiconductor) , artificial neural network , quality (philosophy) , computer science , control (management) , task (project management) , production (economics) , reliability engineering , refrigeration , software , industrial engineering , manufacturing engineering , engineering , systems engineering , artificial intelligence , economics , mechanical engineering , power (physics) , philosophy , physics , epistemology , quantum mechanics , macroeconomics , programming language
This article discusses the organizational principles of industrial products quality control, a review of data on the purpose of artificial neural networks, as well as the possibility of their application to solving the task of reliability indicators analysis. In the study, the reliability indicators of refrigeration equipment were analyzed and an algorithm of their analysis is given. The proposed algorithm can be useful for output goods quality control monitoring, as correction of defects in production requires considerable material costs. The algorithm will allow taking timely actions in case of deviation of the specified parameters and as a result reducing the costs of the enterprise. This algorithm is not tied to refrigeration equipment manufacturing and can easily be scaled to other manufacturing. In the future, it is possible to expand this direction in the form of software development for quality control of the output goods.