Methods of Designing Adaptive Systems of Multilevel Monitoring and Diagnosis for Recognition and Forecasting of Technological Condition of Complex Technical Objects

The complex problem of building a multi-level system for monitoring and diagnosing of complex industrial facilities as integrated systems based on the principles of self-organization of complex systems is presented. This problem is especially relevant for large companies, industrial systems and stra...

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Bibliographic Details
Published in:	2021 IEEE 3rd International Conference on Advanced Trends in Information Theory (ATIT) pp. 290 - 293
Main Authors:	Marchenko, Nadiia, Nechyporuk, Olena, Suprun, Olha, Martynova, Oksana, Suprun, Oleh, Melnyk, Maksym
Format:	Conference Proceeding
Language:	English
Published:	IEEE 15-12-2021
Subjects:	Adaptation models Adaptive systems Analytical models automated control systems Complexity theory Industrial facilities multilevel monitoring and diagnostic systems multilevel queuing models performance evaluations Predictive models Process control
Online Access:	Get full text
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Summary:	The complex problem of building a multi-level system for monitoring and diagnosing of complex industrial facilities as integrated systems based on the principles of self-organization of complex systems is presented. This problem is especially relevant for large companies, industrial systems and strategically important objects, since even a minor mistake may lead to great financial loses or even technological catastrophe. Therefore, a reliable system for problems forecasting is required. The proposed two-level system of monitoring and diagnosing the state of complex industrial facilities is proposed, which differs from analogues by the ability to automatically select the optimal modes of operation of subsystems when changing the characteristics of the incoming flow of applications. This improvement allows not only to consider the state of the system, but to predict different future changes, according to expert's predictions.
DOI:	10.1109/ATIT54053.2021.9678591