Intelligent system for monitoring the operational properties of ship power equipment
DOI:
https://doi.org/10.31498/2225-6733.48.2024.310713Keywords:
monitoring, technical operation, ship vehicles, intelligent systems, diagnostics, failure, risks, uncertaintyAbstract
The peculiarity of monitoring the technical condition of ship vehicles is that during the service life the main energetic installation is not changed, but its continuous maintenance and periodic repairs are carried out. In the organization of such activities, the leading role belongs to technical diagnostics, which allows you to determine the technical condition of the material, as well as predict possible changes for a certain period. Intelligent system of monitoring of operational properties of ship power equipment with Markov circuits, probabilistic dynamics elements, calculations on probabilistic models, multi-criteria optimization of diagnostic parameters, simulation and scenario generation is proposed. In detail, the content of the main structural units of the system for monitoring the operational properties of ship power equipment is considered. The input information was a generalization of experience in the operation of vehicles. The stages of construction of Markov circuits are described in relation to diagnostics of turbochargers, the feature of which is a replacement of discrete time by a continuous sequence of states. Considered by a separate unit of calculation on probabilistic models on the basis of spectra of vibration signals by combining their main discrete features and establishing new diagnostic parameters. The visualization of relationships consists in the construction of digraphs of interactions of the main structural elements of the SES taking into account probabilistic models. Multi-criteria optimization in the presented system is considered from the standpoint of statistical criteria and their convolution. Simulation and generation of basic scenarios is described, from the point of view, the conversion of analog data about workflows to digital form. Provided information support and tools of the system for monitoring the operational properties of ship power equipment
References
Варбанец Р. А. Системы компьютерной диагностики судовых дизелей. Судоходство. 2004. No. 6. С. 24-27.
Суворов П. С. Управление режимами работы главных судовых дизелей. Одесса: ЛАТСТАР, 2000. 238 с.
Миська А. Р., Дранкова А. О., Муха Н. И. Информационный подход к мониторингу технического состояния судовых дизель-генераторных установок. Авиационно-космическая техника и технология: Научно-технический журнал. 2010. № 8 (75). С. 136-139.
Sharko O., Yanenko A. Modeling of inteligent software for the diagnosis and monitoring of ship power plant components using Markov chains. Naukoiemni tekhnolohii. 2023. № 3(59). Pp. 251-261. DOI: https://doi.org/10.18372/2310-5461.59.17946.
Неруйнівній контроль і технічна діагностика / за ред. З. Т. Назарчука. Львів: ФМІ ім. Г.В. Карпенка НАНУ, 2001. 1134 с.
Vibration characterization of rolling bearings with compound fault features under multiple interference factors / Y. Wang et al. PLoS ONE. 2024. Vol. 19(2). Pp. 1-19. DOI: https://doi.org/10.1371/journal.pone.0297935.
Шарко О. В., Яненко А. В. Діагностика та вимірювальний контроль елементів суднового енергетичного обладнання в умовах невизначеності характеру навантажень. Дніпровські читання-2022 : матеріали ІІІ Міжн. наук-практ. конф., м. Київ, 8 грудня 2022 р. С. 129-130.
Шарко О. В., Яненко А. В. Сучасні технології діагностування і визначення залишкового ресурсу елементів СЕУ в процесі експлуатації. Сучасні енергетичні установки на транспорті, технології та обладнання для їх обслуговування : матеріали 14-й Міжн. наук.-практ. конф., м. Херсон, 16-18 березня 2023 р. С. 75-77.
Sharko M., Gusarina N., Petrushenko N. Information-entropy model of making management decisions in the economic development of the enterprises. Advances in Intelligent Systems and Computing. 2019. Vol. 1020. Pp. 304-314. DOI: https://doi.org/10.1007/978-3-030-26474-1_22.
A blind deconvolution approach based on spectral harmonics-to-noise ratio for rotating machinery condition monitoring / Q. Zhou et al. IEEE Transactions on Automation Science and Engineering. 2023. Vol. 20. Iss. 2. Pp. 1092-1107. DOI: https://doi.org/10.1109/TASE.2022.3179457.
Research on fault feature extraction and early warning of rolling bearing vibration signal of generator set / Ma Y., Si J., Yan Q., Wang J. Applied Mathematics and Nonlinear Sciences. 2024. Vol. 9. Iss. 1. Pp. 1-14. DOI: https://doi.org/10.2478/amns.2023.2.00338.
Jin Y., Xin G., Antoni J. Towards automated, integrated and unsupervised diagnosis of rolling element bearings. Mechanical Systems and Signal Processing. 2023. Vol. 203. Art. no. 110691. DOI: https://doi.org/10.1016/j.ymssp.2023.110691.
Quantitative analysis of seasonal uncertainty of metro tunnel's long-term longitudinal settlement via hierarchy Bayesian network / M. Zhu et al. Springer Series in Geomechanics and Geoengineering. 2019. Pp. 279-291. DOI: https://doi.org/10.1007/978-3-030-32029-4_24.
Feature extraction and characterization of rolling bearing vibration signal based on multi parameter information fusion and screening. Y.-D. Sha et al. Tuijin Jishu/Journal of Propulsion Technology. 2023. Vol. 44(7). Art. no. 2205050.
Research on micro-turbine operating state characterization based on bearing vibration signals analysis / Li N., Wang X., Yang T., Han Q. International Journal of Acoustics and Vibrations. 2023. Vol. 28 (4). Pp. 435-450. DOI: https://doi.org/10.20855/ijav.2023.28.41997.
Research on rolling bearing state health monitoring and life prediction based on PCA and Internet of things with multi-sensor / Wang H., Ni G., Chen J., Qu J. Measurement: Journal of the International Measurement Confederation. 2020. Vol. 157. Art. no. 107657. DOI: https://doi.org/10.1016/j.measurement.2020.107657.
Momenzadeh M., Sehhati M., Rabbani H. A novel feature selection method for microarray data classification based on hidden Markov model. Journal of Biomedical Informatics. 2019. Vol. 95. Art. no. 103213. DOI: https://doi.org/10.1016/j.jbi.2019.103213.
Ficco M. Detecting IoT malware by Markov chain behavioral models. Proceedings of 2019 IEEE International Conference on Cloud Engineering IC2E 2019, Prague, Czech Republic, 24-27 June, 2019. Pp. 229-234. DOI: https://doi.org/10.1109/IC2E.2019.00037.
Homogenized energy model and Markov chain Monte Carlo simulations for macro fiber composites operating in broadband regimes / Z. Hu et al. Conference on Smart Materials, Adaptive Structures and Intelligent Systems ASME 2012, Stone Mountain, Georgia, USA, 19-21 September, 2012. Vol. 1. Pp. 321-327. DOI: https://doi.org/10.1115/SMASIS2012-7942.
An accurate prediction algorithm of RUL for bearings: time-frequency analysis based on MRCNN / D. Huang et al. Wireless Communications and Mobile Computing. 2022. Pp. 1-13. DOI: https://doi.org/10.1155/2022/2222802.
Remaining useful life prediction method of coated spherical plain bearing based on VMD-EEMD-LSTM / L. Lin et al. Jixie Gongcheng Xuebao/Journal of Mechanical Engineering. 2023. Vol. 59(9). Pp. 125-136. DOI: https://doi.org/10.3901/JME.2023.09.125.
Шарко О. В., Яненко А. В., Мовчан П. В. Система технічної діагностики визначення по-точного стану транспортних пристроїв. Сучасні енергетичні установки на транспорті, технології та обладнання для їх обслуговування СЕУТТО-2024 : матеріали XV Міжн. наук.-практ. конф., м. Херсон, 13-15 березня, 2024 р. С. 39-41.
Monitoring state of marine plain bearings based on exponential degradation model / Sharko A., Sharko O., Stepanchikov D., Yanenko A. CEUR Workshop Proceedings. 2024. Vol. 3664. Pp. 48-58.
Downloads
Published
How to Cite
Issue
Section
License
The journal «Reporter of the Priazovskyi State Technical University. Section: Technical sciences» is published under the CC BY license (Attribution License).
This license allows for the distribution, editing, modification, and use of the work as a basis for derivative works, even for commercial purposes, provided that proper attribution is given. It is the most flexible of all available licenses and is recommended for maximum dissemination and use of non-restricted materials.
Authors who publish in this journal agree to the following terms:
1. Authors retain the copyright of their work and grant the journal the right of first publication under the terms of the Creative Commons Attribution License (CC BY). This license allows others to freely distribute the published work, provided that proper attribution is given to the original authors and the first publication of the work in this journal is acknowledged.
2. Authors are allowed to enter into separate, additional agreements for non-exclusive distribution of the work in the same form as published in this journal (e.g., depositing it in an institutional repository or including it in a monograph), provided that a reference to the first publication in this journal is maintained.
