Diagnostic engineering of cone crusher drive based on neural networks

The article examines the cone crusher drive and illustrates probability of diagnostic engineering of the drive components using neural networks and Access data base. Specific requirements imposed on the cone crusher drives are discussed. The cone crushers operate drive capable to operate under dynamic loads with allowance for smooth adjustment of velocities within wide ranges. The use of neural network in diagnosis and control of KMD drive operation can enable fast detection and localization of alarm conditions, as well as elimination of faults during operation. Neural network-based diagnosis provides correct evaluation of availability indexes of assemblies and units. Applicability of artificial neural networks in KMD drive diagnosis and operation prediction is demonstrated.

Keywords: cone crushers, neural networks, mining machines, prediction, duty analysis, program analysis.
For citation:

Ibraeva N. R., Lagunova Yu. A. Diagnostic engineering of cone crusher drive based on neural networks. MIAB. Mining Inf. Anal. Bull. 2021;(11-1):162—170. [In Russ]. DOI: 10.25018/0236_1493_2021_111_0_162.

 

Acknowledgements:
Issue number: 11
Year: 2021
Page number: 162-170
ISBN: 0236-1493
UDK: 621.926.1
DOI: 10.25018/0236_1493_2021_111_0_162
Article receipt date: 01.06.2021
Date of review receipt: 10.09.2021
Date of the editorial board′s decision on the article′s publishing: 10.10.2021
About authors:

Ibraeva N. R.1,2, Lecturer at the Process Equipment, Machine Engineering and Standardization Department, Post-Graduate Student at the Department of Mining Machines and Assemblies;
Lagunova Yu. A.2, Dr. Sci. (Eng.), Professor, Department of Mining Machines and Assemblies, e-mail: yu.lagunova@mail.ru;
1 Karaganda Technical University, Karaganda, Kazakhstan;
2 Ural State Mining University, Yekaterinburg, Russia

 

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