TY - JOUR
T1 - Automated anomalies detection in the work of industrial robots
AU - Goncharov, A.
AU - Savelev, A.
AU - Krinitsyn, N.
AU - Mikhalevich, S.
N1 - Funding Information:
This work was funded as part of Federal government-sponsored program “Science” number 2.5760.2017/БЧ by Tomsk Polytechnic University.
Publisher Copyright:
© 2021 Institute of Physics Publishing. All rights reserved.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/1/20
Y1 - 2021/1/20
N2 - This article describes the results of the anomalies automated detection algorithm development in the operation of industrial robots. The development of robotic systems, in particular, industrial robots, and software for them is ahead of the tracking and managing technologies development. The operation of the digital production system involves the generation of a large amount of various data characterizing the state of both the specific equipment and the industrial system as a whole. Such a system produces a sufficient amount of data to develop machine learning models to analyse this data to solve problems such as forecasting and modelling. As part of the study, an experiment was conducted based on the equipment of the laboratory of industrial robots of Tomsk Polytechnic University. In the course of the research, the industrial manipulator moved loads belonging to different classes by weight. An algorithm was developed for the automated analysis of the values of the parameters of the consumed current and the position of the manipulator.
AB - This article describes the results of the anomalies automated detection algorithm development in the operation of industrial robots. The development of robotic systems, in particular, industrial robots, and software for them is ahead of the tracking and managing technologies development. The operation of the digital production system involves the generation of a large amount of various data characterizing the state of both the specific equipment and the industrial system as a whole. Such a system produces a sufficient amount of data to develop machine learning models to analyse this data to solve problems such as forecasting and modelling. As part of the study, an experiment was conducted based on the equipment of the laboratory of industrial robots of Tomsk Polytechnic University. In the course of the research, the industrial manipulator moved loads belonging to different classes by weight. An algorithm was developed for the automated analysis of the values of the parameters of the consumed current and the position of the manipulator.
UR - http://www.scopus.com/inward/record.url?scp=85100344290&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85100344290&partnerID=8YFLogxK
U2 - 10.1088/1757-899X/1019/1/012095
DO - 10.1088/1757-899X/1019/1/012095
M3 - Conference article
AN - SCOPUS:85100344290
VL - 1019
JO - IOP Conference Series: Materials Science and Engineering
JF - IOP Conference Series: Materials Science and Engineering
SN - 1757-8981
IS - 1
M1 - 012095
T2 - 14th International Forum on Strategic Technology, IFOST 2019
Y2 - 14 October 2019 through 17 October 2019
ER -