Method for Defining Parameters of Electromechanical System Model as Part of Digital Twin of Rolling Mill

Gasiyarov, Vadim R.; Radionov, Andrey A.; Loginov, Boris M.; Zinchenko, Mark A.; Gasiyarova, Olga A.; Karandaev, Alexander S.; Khramshin, Vadim R.

doi:10.3390/jmmp7050183

Cited by 7 publications

(3 citation statements)

References 61 publications

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“…The problem of maintaining the performance of plate rolling stand electromechanical systems operating in the reverse mode with periodic accelerations and decelerations and shock loading is relevant. High dynamic loads lead to the early wear of equipment, which causes significant losses for the enterprise [12,13]. The most common failures occur in spindle joints, ensuring a mechanical connection between the spindle, roll, and motor.…”

Section: Causes Of Intensive Wear Of Spindle Jointsmentioning

confidence: 99%

Telemetry System to Monitor Elastic Torque on Rolling Stand Spindles

Voronin,

Loginov,

Gasiyarova

et al. 2024

JMMP

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This article outlines the relevance of building online telemetry systems for online monitoring of the technical conditions of rolling mill equipment. Electromechanical systems of the horizontal stand of the plate Mill 5000 are described, when operating in harsh conditions caused by the shock loading when workpieces enter the stand. It is noted that dynamic torque overloads, exceeding the rated motor torque by many-fold, cause the fatigue failure of spindle joints and breakage of rolls. In this regard, the development and implementation of systems for monitoring the elastic torque on spindles are extremely urgent. This issue has long been studied, but the references provide no information on the building principles and hardware composition of such systems. The use of strain gauges connected according to a balanced bridge circuit to measure the elastic torque is justified. This paper’s contribution is the proposed modular principle for building a telemetry monitoring system based on the analysis of known techniques for measuring and transmitting diagnostic data. The developed system structure is provided and the concept of data transfer and processing are explained. This article suggests the inductive power supply of a measuring unit mounted on a shaft without the use of batteries. A hardware structure was developed to be applied in a system for measuring, transmitting, and visualizing signals proportional to the elastic torque, manufactured on the basis of data measuring instruments by leading companies. The specifics of placement and connection of strain gauges are considered. The hardware providing a wireless power supply to the signal encoder and digital data transfer between the transmitter and receiver is described. The results of implementing the system on Mill 5000 are provided. The installation of a telemetry ring and a receiving head for the inductive power supply and data reception is shown. An experimental assessment of the elastic torques occurring when workpieces enter the cage was obtained by implementing a drive control algorithm which provided biting in the drive acceleration mode. The reliability of measuring the elastic torque with an error not exceeding ±5% and the reduction of dynamic loads on the spindle by 1.3–1.5 times due to the elimination of impacts from closing angular gaps in spindle joints was confirmed. This increases the service life of mechanical equipment and reduces the cost of eliminating the accident aftermath. The prospect of modifying the developed system into a cyber-physical system for monitoring the rolling mill’s mechatronic equipment conditions is shown.

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Section: Causes Of Intensive Wear Of Spindle Jointsmentioning

confidence: 99%

Telemetry System to Monitor Elastic Torque on Rolling Stand Spindles

Voronin,

Loginov,

Gasiyarova

et al. 2024

JMMP

Self Cite

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“…Gasiyarov, V.R. et al [22] proposed a method for defining the two-mass system model parameters using the oscillograms obtained in the operating and emergency modes. The method was developed for the horizontal stand drives of a 5000 mm plate mill and is supported by numerical examples which have been applied to the development of an observer of the elastic torque of the rolling stand's electromechanical system.…”

Section: Introductionmentioning

confidence: 99%

Digital Model of Plan View Pattern Control for Plate Mills Based on Machine Vision and the DBO-RBF Algorithm

Jiao,

Gao,

Liu

et al. 2024

Metals

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Plan view pattern control (PVPC) is a highly effective means to improve the rectangularization of products and increase the yield of plate mills. By optimizing the parameters of PVPC, the effect of PVPC can be further improved. In this paper, a digital model for predicting and controlling crop patterns of plates is proposed based on the radial basis function (RBF) neural network optimized by the dung beetle optimizer (DBO) algorithm. Machine vision technology is used to obtain a digital description of the crop pattern of the rolled plates. An automatic threshold adjustment algorithm is proposed for the image processing of plate pattern photos during the rolling process. The error between the pattern data calculated through machine vision technology and the measured pattern data does not exceed 3 mm. The spread parameters of the RBF are optimized using DBO, and the digital model structure is established. The goodness of fit (R2) and the mean absolute error (MAE) are used as evaluation indicators. The results show that the digital model established based on DBO-RBF has good predictive and control performance, realizing intelligent prediction of the crop pattern of plates and the parameter optimization of PVPC. In practical applications, the crop cutting loss area of irregular deformation at the end of the plate can be reduced by 31%.

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“…The proposed approaches are applicable to cases where the mechanical system is flexible. A method is proposed for determining the parameters of a model of a two-mass electromechanical system based on oscillograms obtained in operating and emergency modes [21]. The technique is universal and includes the calculation of the moments of inertia of rotating masses, the coefficients of elastic rigidity and vibration damping, as well as the time constants of the motor air gap torque control circuit.…”

Section: Introductionmentioning

confidence: 99%

Stability Assessment of an Ore Mill Electric Drive Using Machine Learning

Baghdasaryan,

Hovhannisyn

2024

HighTech. Innov. J.

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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 Full Text: PDF

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Method for Defining Parameters of Electromechanical System Model as Part of Digital Twin of Rolling Mill

Cited by 7 publications

References 61 publications

Telemetry System to Monitor Elastic Torque on Rolling Stand Spindles

Telemetry System to Monitor Elastic Torque on Rolling Stand Spindles

Digital Model of Plan View Pattern Control for Plate Mills Based on Machine Vision and the DBO-RBF Algorithm

Stability Assessment of an Ore Mill Electric Drive Using Machine Learning

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