Diagnostic engineering of cone crusher drive based on neural networks
К ВОПРОСУ ДИАГНОСТИКИ ТЕХНИЧЕСКОГО СОСТОЯНИЯ ПРИВОДА КОНУСНОЙ ДРОБИЛКИ НА ОСНОВЕ НЕЙРОННЫХ СЕТЕЙ
Ibraeva N.R. Lagunova Yu.A.
2021Publishing house Mining book
Mining Informational and Analytical Bulletin
2021#2021Issue 1-11162 - 170 pp.
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.
Cone crushers , Duty analysis , Mining machines , Neural networks , Prediction , Program analysis
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Karaganda Technical University, Karaganda, Kazakhstan
Ural State Mining University, Yekaterinburg, Russian Federation
Karaganda Technical University
Ural State Mining University
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026