Identification of the state of dynamic objects from measured parameters. From theory to application

Новоселов, О. Н.

doi:10.1007/s11018-010-9473-2

Cited by 3 publications

(4 citation statements)

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“…Математична модель (7) вказує на високий рівень перехресних впливів в робочій тепловій зоні екструдера за винятком параметру W 13 (p).…”

Section: постановкаunclassified

“…Це, в першу чергу, впливає на постійні часу агрегату, знижує його мобільність, призводить до додаткових втрат сировини і фінансів. Для усунення названих недоліків необхідно привести модель (7) до вигляду [16]:…”

Section: постановкаunclassified

“…В статті проаналізоване обладнання, яке виконує процес гранулювання зі стренговою порізкою, основним елементом якого є двохшнековий екструдер. Виявлені найбільш вразливі місця екструдера і отримані математичні моделі вібраційних процесів (3) і теплових процесів (7). Розроблена модель компенсатора перехресних зв'язків (10).…”

Section: постановкаunclassified

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Obtaining Mathematical Models of Multi-Zone Technological Units Through Practical Identification

Mygushchenko,

Kropachek,

Korzhov

et al. 2022

HM and HU

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Modern industrial equipment represents an extensive conjuncture of industrial and agricultural objects. Such objects require control of technical and technological processes during their operation and the ability to manage said processes in real time. The construction of control and management systems of industrial objects requires the study of such objects, and this is impossible without the presence of an appropriate mathematical model of the processes occurring in the equipment. It is known that any processes can be described by differential equations of the appropriate order. However, practical work with such equations at the production level is extremely complicated. The transformation of differential equations into algebraic ones using the Laplace transformation allows for theoretical or simulated modeling in production conditions. The most common processes occurring in industrial facilities are thermal and vibration processes. Mathematical models, in the form of transfer functions, represent aperiodic links with a delay and oscillating links. Mathematical models make it possible to evaluate relevant processes in technical (technological) objects, to determine the static and dynamic characteristics of processes, to identify the level of mutual influence of blocks of objects, to conduct simulation modeling of processes, etc. Traditionally, there are two possible methods of obtaining mathematical models of processes in industrial facilities - the theoretical one, based on the construction of equations based on a priori information with the subsequent verification of the result, and the methods of practical identification, based on the removal of the initial characteristics of the object in practice. Taking into account the availability of the research object, the authors chose the second way. As an industrial facility, a two-screw extruder manufactured by Scientific and Production Enterprise "PAKS" LLC, Kharkiv, was adopted, which can be used as part of granulation equipment with a string cut. The authors performed work on the practical identification of an industrial object, obtained and analyzed mathematical models of processes in the object, provided recommendations for creating an information and measurement system of the selected equipment.

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Section: постановкаunclassified

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Obtaining Mathematical Models of Multi-Zone Technological Units Through Practical Identification

Mygushchenko,

Kropachek,

Korzhov

et al. 2022

HM and HU

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“…Possible States of an Object. An analysis shows [9,10] that the parameters of the state of an object are the coefficients of the fi rst degree in Eq. (2).…”

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Prediction of the State of Dynamic Systems Based on Measurement Data

Новоселов

Gufeld

2015

Meas Tech

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A mathematical method is proposed for predicting the state of a dynamic system based on arriving measurement data. This method is based on early detection of the hidden evolution of the system and its transition to an extreme state by evaluating the stability and catastrophic states of the system with each new measurement. The method is illustrated by an example of predicting an earthquake in Kamchatka and the period during which it is hazardous.

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General Properties of Dynamical Systems: From Measurement to Equation

Новоселов

2014

Meas Tech

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Identification of the state of dynamic objects from measured parameters. From theory to application

Cited by 3 publications

References 1 publication

Obtaining Mathematical Models of Multi-Zone Technological Units Through Practical Identification

Obtaining Mathematical Models of Multi-Zone Technological Units Through Practical Identification

Prediction of the State of Dynamic Systems Based on Measurement Data

General Properties of Dynamical Systems: From Measurement to Equation

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