An in-place method of forming a database on the technical condition of the internal combustion engine fuel system of technological machines in real time

封面

如何引用文章

全文:

详细

BACKGROUND: The economic component of the effective functioning of technological machines consists in their uninterrupted operation when performing various tasks in such industries as construction, road, agriculture, etc. It is currently possible to take into account the variety of operating conditions of technological machines and constantly changing loading modes by using digital technologies that allow creating an array of databases not only in real time, but also for an individual machine. A relevant task is to create methods for collecting information about the operating modes of the machine, the factors causing changes in the efficiency of operation, the development of algorithms for making decisions about maintaining the working condition of all units and systems of the machine, preventing their failures and non-production downtime of the machine as a whole.

OBJECTIVE: Ensuring the effective functioning of a single technological machine by managing the risks of failures, by adjusting the frequency of maintenance and periods of repair-and-restoration activities based on the real-time data on the technical condition and digital processing of decision-making information.

METHODS: The nature of changes in the technical condition of units and systems of technological machines in the theory of systems is most often considered as random due to the high probability of uncertainty of factorial influence. It is proposed to consider the problem of failure risk management of system elements and units of technological machines using the basic provisions of the excursion theory. The object of the study is a diesel engine of a technological machine with an example of monitoring the technical condition of the fuel system.

RESULTS: The justification of the expediency of performing maintenance and repair-and-restoration activities (MaR) of technological machines as required is presented. The adjustment of the MaR frequency was carried out based on the data of changes in the performance of the machine, in particular the internal combustion engine fuel system. To develop an algorithm for the formation of a data array, typical architectures for collecting and processing information using digital platforms for decision-making on the intensity of parameter changes are used, as an example, oscillograms of pressure changes during the operation of the internal combustion engine fuel system are presented.

CONCLUSIONS: A module for diagnosing the technical condition and efficiency of the internal combustion engine of a single technological machine in real time has been developed. It is proposed to introduce an intelligent decision-making system with subsequent transformation into the form of a digital twin of failure risk management and control of the efficiency of the machine for various operating conditions.

作者简介

Alexey Arzhenovsky

Russian State Agrarian University - Moscow Timiryazev Agricultural Academy

Email: arzhenovski@rgau-msha.ru
ORCID iD: 0000-0002-3569-8934
SPIN 代码: 5549-4841

Dr. Sci. (Engineering), Associate Professor, Acting Director of the Institute of Mechanics and Power Engineering named after V.P. Goryachkin

俄罗斯联邦, Moscow

Nadezhda Sevryugina

Russian State Agrarian University - Moscow Timiryazev Agricultural Academy

编辑信件的主要联系方式.
Email: nssevr@yandex.ru
ORCID iD: 0000-0002-3494-1437
SPIN 代码: 4444-0443

Dr. Sci. (Engineering), Associate Professor, Professor of the Technical Service of Machinery and Equipment Department

俄罗斯联邦, Moscow

Alexey Apatenko

Russian State Agrarian University - Moscow Timiryazev Agricultural Academy

Email: a.apatenko@rgau-msha.ru
ORCID iD: 0000-0002-2492-9274
SPIN 代码: 7553-2715

Dr. Sci. (Engineering), Associate Professor, Head of the Technical Service of Machinery and Equipment Department

俄罗斯联邦, Moscow

参考

  1. Pastukhov AG, Timashov EP, Bakharev DN. Generalized assessment of the main factors in the design of machinery and technologies in agroengineering. Innovations in agriculture: problems and prospects. 2021;1(29):17-26. (In Russ.)
  2. Kutuzov VV. Efficiency of operation of construction and road vehicles, taking into account changes in their technical condition. Technology of wheeled and tracked vehicles. 2015;3(19):57–64. (In Russ.)
  3. Dalsky N. Restoration of agricultural machinery — a new life of aggregates! Our agriculture. 2023;13(309):64–67. (In Russ.)
  4. Kosenko EA, Baurova NI, Zorin VA. Service Properties of Composites with Various Types of Hybrid Matrices. Russian Metallurgy (Metally). 2020;13:1526–1530. doi: 10.1134/S0036029520130169
  5. Lebedev AT, Arzhenovskiy AG, Chayka YeA, et al. Methodology for Assessing the Efficiency of Measures for the Operational Management of the Technical Systems’ Reliability. In: XIV International Scientific Conference “INTERAGROMASH 2021”: Precision Agriculture and Agricultural Machinery Industry. Volume 1, Rostov-on-Don, 24–26.02.2021. Berlin: Springer Verlag; 2022;242:13–20. doi: 10.1007/978-3-030-81619-3_2
  6. Kravchenko IN, Erofeev MN, Zorin VA. Methodology for calculating optimal volumes and nomenclature of spare parts of machines and technological equipment for the production of building materials. Repair. Recovery. Modernization. 2008;11:33–36. (In Russ.)
  7. Golubev IG, Sevryugina NS, Apatenko AS, et al. Modernizing Machines to Extend Their Life. Russian Engineering Research. 2023;43(3):258–263. doi: 10.3103/s1068798x23040111 EDN: TZDWXO
  8. Leonov OA, Temasova GN. Building a functional model of the process “Maintenance and repair of agricultural machinery” from the perspective of the requirements of international standards for quality management systems. Bulletin of FGOU VPO “MGAIU named after V.P. Goryachkin”. 2009;7(38):35–40. (In Russ.)
  9. Starostin IA, Lavrov AV, Eshchin AV, et al. The state and prospects of development of the agricultural tractor fleet in the context of digital transformation of agricultur. Tractors and agricultural machinery. 2023;90(4):387–394. (In Russ.) doi: 10.17816/0321-4443-567790
  10. Golubev IG, Bykov VV, Golubev MI. Promising directions of using digital solutions in the technical service of machines in the forest complex. In: Annual national NTC of teaching staff, postgraduates and students of the Mytishchi branch of the Bauman Moscow State Technical University based on the results of research for 2020: Collection of abstracts, Mytishchi, Moscow region, 01-03.02.2021. Krasnoyarsk: NITs; 2021;42–44. (In Russ.)
  11. Kalashnikov PV. Mathematical model of risk control arising from the functioning of complex technical systems for critical purposes in conditions of uncertainty of information about the values of parameters and the phase state. International Journal of Advanced Studies. 2022;12(3):22–39. doi: 10.12731/2227-930X-2022-12-3-22-39
  12. Zhiyao Zhang, Xiaohui Chen, Enrico Zio, et al. Multi-task learning boosted predictions of the remaining useful life of aero-engines under scenarios of working-condition shift. Reliability Engineering & System Safety. 2023;237. doi: 10.1016/j.ress.2023.109350
  13. Komarov VA. Criteria for the limiting state of machine units: theoretical background. Tractors and agricultural machinery. 2005;2:28–30. (In Russ.)
  14. Bugrimov VA, Kondratiev AV, Sarbaev VI. Modeling of the inventory management system of a service station. The world of transport and technological machines. 2017;4(59):9–16.
  15. Apatenko AS, Vladimirova NI. Analysis of systems of repair and preventive maintenance of technological machines. Bulletin of the Federal State Educational Institution of Higher Professional Education “V.P. Goryachkin Moscow State Agroengineering University”. 2013;1(57):72–76. (In Russ.)
  16. Sevryugina NS. Integration of the probability theory of random processes in the information and analytical complex for monitoring the performance of road vehicles. In: Interstroymeh — 2015: materials of the ISTC, Kazan, 09-11.09.2015. KGASU. Kazan: KGASU; 2015:188–192. (In Russ.)
  17. Arzhenovsky AG, Asaturyan SV. Improvement of methods and means for determining energy and fuel-economic indicators of tractor engines — Zernograd: Azov-Black Sea Engineering Institute — branch of the Federal State budgetary educational institution of Higher education “Don State Agrarian University” in Zernograd. Zernograd; 2013. (In Russ.)
  18. Leonov OA, Shkaruba NJ. Calculation of landing tolerance based on the parametric connection failure model. Problems of mechanical engineering and automation. 2020;4:14–20. (In Russ.)
  19. Sevryugina NS, Ruzanov EV, Matveenko MA, et al. Embedded multiplex digital monitoring system for environmental management machines. In: Scientific and information support for innovative development of the agro-industrial complex : materials XI MNPK, 05–07.06.2019. Pravdinsky: Rosinformagrotech; 2019:378–383. (In Russ.)
  20. Kulmanakov SP, Tyutikov SA. Assessment of the effect of fuel pressure pulsation in the Common Rail system on the economic and environmental performance of diesel. Tractors and agricultural machinery. 2023;90(3):201–206. (In Russ.) doi: 10.17816/0321-4443-241226
  21. Lebedev AT, Arzhenovskiy A, Zhurba VV, et al. Operational Management of Reliability of Technical Systems in the Agro-Industrial Complex. In: XIV International Scientific Conference “INTERAGROMASH 2021”: Precision Agriculture and Agricultural Machinery Industry. Rostov-on-Don, 24–26.02.2021. Berlin: Springer Verlag; 2022:79–87. doi: 10.1007/978-3-030-81619-3_9
  22. Shimokhin AV, Kirasirov OM. Mechanism for improving the management of the maintenance and repair process using neural network technology. Tractors and agricultural machinery. 2023;90(6):561–573. (In Russ.) doi: 10.17816/0321-4443-546006

补充文件

附件文件
动作
1. JATS XML
2. Fig. 1. Excursions of a random process: H — the local maximum of the process X(t); Hm — the absolute maximum of the system function X(t); τ0 — the time of the first excursion; τ — the duration of the positive excursion (the period of normal operation of the units); θ — the duration of the negative excursion (operation of a faulty system in the mode of operation efficiency decreasing).

下载 (46KB)
3. Fig. 2. “Theoretical” oscillogram of pressure changes in the fuel line of the internal combustion engine power fuel system.

下载 (69KB)
4. Fig. 3. The “reference” oscillogram of the operation of the internal combustion engine fuel system.

下载 (42KB)
5. Fig. 4. Mounting of sensors: а — the casing of the A-41 internal combustion engine; b — the casing of the SMD-62 internal combustion engine; c — the fuel line of the nozzle of the 1st cylinder; d — the intake manifold; 1 — the flywheel speed sensor on the casing of the internal combustion engine; 2 — the sensor for fixing the position of the piston in the top dead center; 3 — the pressure sensor in fuel line; 4 — the air boost pressure sensor.

下载 (290KB)
6. Fig. 5. Infograms of the control of the elements of the internal combustion engine fuel system by the indicator of pressure change at: а — wear of the discharge valve; b — wear of the plunger pair; c — total wear of the discharge valve and the plunger pair; d — decrease in the pressure of the beginning of lifting the nozzle needle.

下载 (209KB)
7. Fig. 6. The results of digital monitoring of the state of the internal combustion engine: point A — the moment of the beginning of an increase in pressure in the fuel line; point B — the position of the piston of the first cylinder at the top dead center of the compression stroke; γ — the angle of advance of the fuel supply is determined by calculation.

下载 (199KB)

版权所有 © Eco-Vector, 2024

Creative Commons License
此作品已接受知识共享署名-非商业性使用-禁止演绎 4.0国际许可协议的许可。
 


Согласие на обработку персональных данных с помощью сервиса «Яндекс.Метрика»

1. Я (далее – «Пользователь» или «Субъект персональных данных»), осуществляя использование сайта https://journals.rcsi.science/ (далее – «Сайт»), подтверждая свою полную дееспособность даю согласие на обработку персональных данных с использованием средств автоматизации Оператору - федеральному государственному бюджетному учреждению «Российский центр научной информации» (РЦНИ), далее – «Оператор», расположенному по адресу: 119991, г. Москва, Ленинский просп., д.32А, со следующими условиями.

2. Категории обрабатываемых данных: файлы «cookies» (куки-файлы). Файлы «cookie» – это небольшой текстовый файл, который веб-сервер может хранить в браузере Пользователя. Данные файлы веб-сервер загружает на устройство Пользователя при посещении им Сайта. При каждом следующем посещении Пользователем Сайта «cookie» файлы отправляются на Сайт Оператора. Данные файлы позволяют Сайту распознавать устройство Пользователя. Содержимое такого файла может как относиться, так и не относиться к персональным данным, в зависимости от того, содержит ли такой файл персональные данные или содержит обезличенные технические данные.

3. Цель обработки персональных данных: анализ пользовательской активности с помощью сервиса «Яндекс.Метрика».

4. Категории субъектов персональных данных: все Пользователи Сайта, которые дали согласие на обработку файлов «cookie».

5. Способы обработки: сбор, запись, систематизация, накопление, хранение, уточнение (обновление, изменение), извлечение, использование, передача (доступ, предоставление), блокирование, удаление, уничтожение персональных данных.

6. Срок обработки и хранения: до получения от Субъекта персональных данных требования о прекращении обработки/отзыва согласия.

7. Способ отзыва: заявление об отзыве в письменном виде путём его направления на адрес электронной почты Оператора: info@rcsi.science или путем письменного обращения по юридическому адресу: 119991, г. Москва, Ленинский просп., д.32А

8. Субъект персональных данных вправе запретить своему оборудованию прием этих данных или ограничить прием этих данных. При отказе от получения таких данных или при ограничении приема данных некоторые функции Сайта могут работать некорректно. Субъект персональных данных обязуется сам настроить свое оборудование таким способом, чтобы оно обеспечивало адекватный его желаниям режим работы и уровень защиты данных файлов «cookie», Оператор не предоставляет технологических и правовых консультаций на темы подобного характера.

9. Порядок уничтожения персональных данных при достижении цели их обработки или при наступлении иных законных оснований определяется Оператором в соответствии с законодательством Российской Федерации.

10. Я согласен/согласна квалифицировать в качестве своей простой электронной подписи под настоящим Согласием и под Политикой обработки персональных данных выполнение мною следующего действия на сайте: https://journals.rcsi.science/ нажатие мною на интерфейсе с текстом: «Сайт использует сервис «Яндекс.Метрика» (который использует файлы «cookie») на элемент с текстом «Принять и продолжить».