Сучасні інформаційні системи

Постійне посилання на розділhttps://repository.kpi.kharkov.ua/handle/KhPI-Press/62915

У журналі публікуються результати досліджень з експлуатації та розробки сучасних інформаційних систем у різних проблемних галузях.

Рік заснування: 2017. Періодичність: 4 рази на рік. ISSN 2522-9052 (Print)

Новини

Включений до "Переліку наукових фахових видань України, в яких можуть публікуватися результати дисертаційних робіт на здобуття наукових ступенів доктора і кандидата наук" (технічні науки) наказом Міністерства освіти і науки України від 04.04.2018 № 326 (додаток 9, п. 56).

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Diagnosis methods for mechanisms and machines based on empirical mode decomposition of a vibrosignal and the wilcoxon test
(Національний технічний університет "Харківський політехнічний інститут", 2022) Zuev, Andrey; Ivashko, Andrey; Lunin, Denis
Methods for diagnosing mechanisms and machines based on the analysis of vibration signals are considered. In particular, the comparison of various algorithms for analyzing vibration signals in the time and frequency domains was made, methods for selecting diagnostic features and methods for secondary processing were analyzed. The purpose of the study is to develop algorithms for selecting the vibration signal envelope based on empirical mode decomposition and decomposition of the signal into intrinsic mode functions, algorithms for the spectral estimation of envelopes and to choose a criterion for making a decision on object classification. It is proposed to choose the non-parametric Wilcoxon signed-rank test to determine the statistical significance of the difference between the parameters of normal and faulty objects. The multichannel microcontroller system for collecting data from an accelerometer and transmitting it to a computer via a local Wi-Fi network, including a number of independent data gathering nodes connected to a common distributed computing system, has been developed and experimentally studied. The computer processing of the recorded vibration signals for serviceable and faulty mechanisms was performed, including data decoding, Hilbert-Huang transform, spectral analysis using the Welch and Yule-Walker methods, and the choice of a diagnostic feature that provides maximum reliability of recognition. Based on the results of the work, it was determined that the empirical mode decomposition makes it possible to obtain vibration signal envelopes suitable for further diagnostics. Recommendations are developed for choosing the intrinsic mode function and the spectral analysis algorithm, it is determined that the first intrinsic mode function is the most informative for the mechanism under study. In accordance with the Wilcoxon criterion, the degree of diagnostic reliability was numerically determined in the analysis of the spectral power density of the vibration signal and the amplitude of peaks, and the comparison of probabilities of error-free recognition for various modifications of the algorithm was made.
Motion capture with MEMS sensors
(Національний технічний університет "Харківський політехнічний інститут", 2023) Dashkov, Dmytro; Liashenko, Oleksii
The object of this article is the registration and analysis of human movements based on sensors. This paper presents a comparison of the basic methods of data processing from inertial micromechanical sensors to collect data a device was implemented that captures movements. As result the device uses the motion data from accelerometer and gyroscope to calculate the motion trajectory: the angle of rotation and acceleration. The data is read by the microcontroller, after which it is filtered and processed by one of the filters (Complementary, Kalman), and finally transferred to a computer for further analysis and display. The purpose of the article is to compare several methods of data processing from microelectromechanical. The results obtained: device was developed, obtained data that can be used to characterize the methods and analyze their work in the system. Conclusions: In the course of the study, a device was developed for collecting and processing data from MEMS sensors, which showed the effectiveness of the complementary filter in comparison with the Kalman filter in real-time systems with limited computing power. Real results confirmed that the results of the complementary method using less computational resources are not far behind the more costly Kalman filter without the use of auxiliary sensors, like a digital compass.