Stability Assessment of an Ore Mill Electric Drive Using Machine Learning

Marinka Baghdasaryan, Vardan Hovhannisyn


The relevance of the study is due to the need to improve electric drive systems operated in harsh conditions. The goal of the study is to create a model for assessing the state of stability of the electric drive of an ore mill using machine learning capabilities, which will provide high performance and the ability to work consistently in different systems. Various sustainability assessment models have been developed based on 6 machine learning algorithms. The study and comparison of models built using artificial neural networks (ANN) of different architectures was carried out using various learning methods. The expediency of using the Tree and ANN algorithms to develop a model for assessing electric drive stability is substantiated. The novelty of the results obtained lies in the fact that the model has high accuracy, high speed, and the ability to detect instability in uncertain operating modes of the electric motor of an electric drive, as well as the possibility of coordinated operation with various systems. The practical value is that the model allows, at an intellectual level, to provide effective control and fault diagnosis of complex electric drive systems, which cannot be achieved using the known methods.


Doi: 10.28991/HIJ-2024-05-02-01

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Machine Learning; Neural Network; Ore Mill; Electric Drive; Intelligent Model Discipline.


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DOI: 10.28991/HIJ-2024-05-02-01


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