[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123891-en":3,"doc-seo-123891-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123891,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Stability Assessment of an Ore Mill Electric Drive Using Machine Learning","The study addresses the need to enhance electric drive systems operating under harsh conditions by building a machine-learning model that evaluates the stability state of an ore mill electric drive. The research compares sustainability assessment models based on six machine-learning algorithms and evaluates artificial neural networks (ANN) with different architectures using various learning methods. Results substantiate the usefulness of Tree and ANN approaches, delivering high accuracy and speed, enabling detection of instability in uncertain operating modes and supporting coordinated operation across different systems.","Stability Assessment of an Ore Mill Electric Drive Using  \nMachine Learning  \nMarinka Baghdasaryan 1*, Vardan Hovhannisyn 1  \n1 Institute of Energetics and Electrical Engineering, National Polytechnic University of Armenia, 105, Teryan St., 0009 Yerevan, Armenia.  \nReceived 21 February 2024; Revised 17 May 2024; Accepted 23 May 2024; Published 01 June 2024  \nAbstract  \nThe 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 ofthe 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.  \nKeywords: Machine Learning; Neural Network; Ore Mill; Electric Drive; Intelligent Model Discipline.  \n1. Introduction  \nThe correct organization of technological processes at manufacturing enterprises is mainly due to the smooth operation and efficient operation of electric drive systems that ensure the operation of the technological mechanisms [1– 5]. An electric drive system is a complex system operating under load, the mechanical and electrical parts of which are in constant interaction. The electrical part of the system consists of an energy accumulator and a converter connected by an electric and magnetic connection. The mechanical part is an inertial mass connected by elastic mechanical joints [6, 7] . During operation, the elastic links in the mechanical part of the electric drive system are subjected to mechanical shocks, which change with a certain frequency and lead to an increase in the wear rate of the structural components of the system and prevent the stable operation of the system. They are especially undesirable for systems operating with variable loads [8, 9] . Such is the electric drive system that ensures the operation of the ore mill; it is energy-intensive and operated in difficult conditions.  \nStudies show that ore mills used in various technological processes operate with an arbitrarily varying load [5, 10– 12] . The random nature of the load change is due to the qualitative characteristics of the ore, the degree of filling of the crushing drum, and the degree of wear of the lining protecting the walls. The ore grinding mill is mainly started without loading the ore into the mill, which makes it possible to facilitate the operation of the electric drive system to some  \n* [Corresponding author: m.baghdasaryan@seua.am](Corresponding author: m.baghdasaryan@seua.am)  \n [http://dx.doi.org/10.28991/HIJ-2024-05-02-01](http://dx.doi.org/10.28991/HIJ-2024-05-02-01)  \n➢ This is an open access article under the CC-BY license ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)) .© Authors retain all copyrights.  \nextent. Meanwhile, during operation, flickering occurs in the elastic links of the electric drive system due to the dynamic parameters of the mechanical transmission system and random changes in the torque of resistance created by the mill. Flickering in the mechanical part of the system e","cbCaiglOxZYPJx5N","https://ap.wps.com/l/cbCaiglOxZYPJx5N","pdf",1951474,1,18,"English","en",105,"# Introduction\n## Electric drive systems and stability challenges\n## Ore milling load variability and elastic link impacts\n## Motivation and study objective\n## Related approaches and prior work","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To create a machine-learning model that assesses the stability state of an ore mill electric drive, enabling high performance and consistent operation across different systems.\"},{\"question\":\"Which machine-learning approaches are emphasized in the research results?\",\"answer\":\"Tree-based methods and artificial neural networks (ANN) are substantiated as expedient options for developing the electric drive stability assessment model.\"},{\"question\":\"Why is stability assessment particularly important for ore mill electric drives?\",\"answer\":\"Ore mills work under arbitrarily varying loads, and shocks or flickering in elastic links can accelerate wear and deformation, increasing the likelihood of emergency states and system failure.\"}]","Stability Assessment of an Ore Mill Electric Drive Using Machine Learning | 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is the main goal of the study?","Question",{"text":75,"@type":76},"To create a machine-learning model that assesses the stability state of an ore mill electric drive, enabling high performance and consistent operation across different systems.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine-learning approaches are emphasized in the research results?",{"text":80,"@type":76},"Tree-based methods and artificial neural networks (ANN) are substantiated as expedient options for developing the electric drive stability assessment model.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is stability assessment particularly important for ore mill electric drives?",{"text":84,"@type":76},"Ore mills work under arbitrarily varying loads, and shocks or flickering in elastic links can accelerate wear and deformation, increasing the likelihood of emergency states and system 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