Virtual Analyzer of the Voltage and Current Spectrum of the Electric Arc in Electric Arc Furnaces

Simakov, Andrei; Trifonova, M. E.; Gorlenkov, D. V.

doi:10.1134/s0036029521060252

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…Automated electric drive is a crucial element of any technological process. The assessment of its technical condition is an essential and high-priority task for the electric power industry, as well as for some related spheres, since the technical condition impacts the technological process [11,12]. The system of monitoring and the evaluation of the electric drive technical condition is not a unique scientific area.…”

Section: Introductionmentioning

confidence: 99%

A Diagnostic Curve for Online Fault Detection in AC Drives

Koteleva,

Korolev

2024

Energies

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The AC drive is an important component and the most common element of any manufacturing process. A particularly serious task is the proper assessment of the AC drive’s technical condition, as its failure can cause problems for entire units and complexes of industrial enterprises. At present, there are several approaches either to determine electric drives’ condition or to find certain defects. Frequently, these methods require the installation of additional equipment that exceeds the price of the electric drive by several times. In this work, a simple approach is proposed. It includes the use of a diagnostic curve to assess the condition. This diagnostic curve is produced from the measurement results of the current sensors on the drive. Based on the Park vector modification, this is a simple and affordable way to obtain real-time information. The obtained curve can be used for the following purposes: directly for condition assessment by visual monitoring, as a sign for diagnostic systems built on artificial intelligence methods, for dynamic tuning of the drive control system. The article gives the algorithm for obtaining the diagnostic curve, showing its efficiency for model and field experiments. In model experiments, the faults in the rotor and stator of the drive were simulated; in field experiments, the state was analyzed by changing the load on the motor.

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Section: Introductionmentioning

confidence: 99%

A Diagnostic Curve for Online Fault Detection in AC Drives

Koteleva,

Korolev

2024

Energies

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“…Such research includes work in the field of equipment fault investigation [41][42][43][44][45]. For example, in [46], scientists determine the nature of defects in induction motor bearings by analyzing current and voltage [47]. The work [48] describes the method's applications in order to suppress noise in the signals coming from the antenna.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Maintenance Scenarios for Equipment and Control Systems in Industry

Koteleva,

Valnev

2023

Applied Sciences

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The well-known methods of scene extraction on video are focused on analyzing the similarity between frames. However, they do not all analyze the composition of the image scene, which may remain the same during maintenance. Therefore, this paper proposes an algorithm for equipment maintenance scene detection based on human hand tracking. It is based on the assumption that, when servicing technological equipment, it is possible to determine the change in repair action by the position of the service engineer’s hands. Thus, certain information and the algorithm that processes these changes allow us to segment the video into actions performed during the service. We process the time series obtained by moving the hand position using spectral singular value decomposition for multivariate time series. To verify the algorithm, we performed maintenance on the control cabinet of a mining conveyor and recorded the work on a first-person video, which was processed using the developed method. As a result, we obtained some scenes corresponding to opening the control cabinet, de-energizing the unit, and checking the contacts with a multimeter buzzer test. A third-person video of motor service was similarly processed. The algorithm demonstrated the results in separate scenes of removing screws, working with a multimeter, and disconnecting and replacing motor parts.

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“…The training sample for training neural networks is supposed to combine with photos taken in a field experiment and software-generated images [36]. In addition, noisy copies of images should be added to improve the reliability of recognition and the robustness of the algorithm to noise [37].…”

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confidence: 99%

The Present Issues of Control Automation for Levitation Metal Melting

Бойков

Payor

2022

Symmetry

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This article is a review of current scientific problems in the field of automation of the electromagnetic levitation melting process control of non-ferrous metals and potential solutions using modern digital technologies. The article describes the technological process of electromagnetic levitation melting as a method of obtaining ultrapure metals and the main problems of the automation of this process taking into account domestic and international experience. Promising approaches to control the position of the melt in the inductor in real time on the basis of vision systems are considered. The main problems and factors preventing the mass introduction of levitation melting in the electromagnetic field to the industry are highlighted. The problem of passing the Curie point by the heated billet and the effect of the billet’s loss of magnetism on the vibrational circuit of the installation and the temperature of the inductor are also considered. The article also reflects key areas of research development in the field of levitation melting, including: optimization of energy costs, stabilization of the position of the melt in the inductor, predictive process control, and scaling of levitation melting units. The concept of a digital twin based on a numerical model as a component of an automatic process control system for the implementation of inductor control and prediction of process parameters of the melt is presented. The possibility of using vision for visual control of the melt position in the inductor based on video images for its further stabilization in the inductor and increasing the accuracy of numerical simulation results by specifying the real geometry of the melt in parallel with the calculation of the model itself is considered.

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Virtual Analyzer of the Voltage and Current Spectrum of the Electric Arc in Electric Arc Furnaces

Cited by 4 publications

References 4 publications

A Diagnostic Curve for Online Fault Detection in AC Drives

A Diagnostic Curve for Online Fault Detection in AC Drives

Automatic Detection of Maintenance Scenarios for Equipment and Control Systems in Industry

The Present Issues of Control Automation for Levitation Metal Melting

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