<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.3 20210610//EN" "JATS-journalpublishing1-3.dtd">
<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">rmrs</journal-id><journal-title-group><journal-title xml:lang="ru">Научно-технический сборник Российского морского регистра судоходства</journal-title><trans-title-group xml:lang="en"><trans-title>Research Bulletin by Russian Maritime Register of Shipping</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">2223-7097</issn><publisher><publisher-name>Российский морской регистр судоходства</publisher-name></publisher></journal-meta><article-meta><article-id custom-type="edn" pub-id-type="custom">JEQRPL</article-id><article-id custom-type="elpub" pub-id-type="custom">rmrs-162</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>МЕХАНИЧЕСКИЕ УСТАНОВКИ И ДВИЖИТЕЛИ</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>MECHANICAL INSTALLATIONS AND PROPULSION</subject></subj-group></article-categories><title-group><article-title>Разработка алгоритма идентификации неисправности индуктивного датчика оборотов</article-title><trans-title-group xml:lang="en"><trans-title>Development of an algorithm for identifying an inductive speed sensor</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Житников</surname><given-names>А. А.</given-names></name><name name-style="western" xml:lang="en"><surname>Zhitnikov</surname><given-names>A. A.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Аспирант.</p><p>683003 Петропавловск-Камчатский, Ключевская ул., 35</p></bio><bio xml:lang="en"><p>PhD student.</p><p>683003 Petropavlovsk-Kamchatsky, Klyuchevskaya ul., 35</p></bio><email xlink:type="simple">zhitnikov-alexandr@mail.ru</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Марченко</surname><given-names>А. А.</given-names></name><name name-style="western" xml:lang="en"><surname>Marchenko</surname><given-names>A. A.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Канд. техн. наук, доцент.</p><p>683003 Петропавловск-Камчатский, Ключевская ул., 35</p></bio><bio xml:lang="en"><p>PhD, Associate Professor.</p><p>683003 Petropavlovsk-Kamchatsky, Klyuchevskaya ul., 35</p></bio><email xlink:type="simple">Marchenko-Alx@inbox.ru</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Камчатский государственный технический университет</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Kamchatka State Technical University</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2025</year></pub-date><pub-date pub-type="epub"><day>02</day><month>02</month><year>2026</year></pub-date><volume>0</volume><issue>81</issue><fpage>96</fpage><lpage>104</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Житников А.А., Марченко А.А., 2026</copyright-statement><copyright-year>2026</copyright-year><copyright-holder xml:lang="ru">Житников А.А., Марченко А.А.</copyright-holder><copyright-holder xml:lang="en">Zhitnikov A.A., Marchenko A.A.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://sbornik.rs-class.org/jour/article/view/162">https://sbornik.rs-class.org/jour/article/view/162</self-uri><abstract><p>В данной работе представлен новый диагностический алгоритм для раннего обнаружения неисправностей в информационноизмерительном канале индуктивных датчиков скорости, используемых в малооборотных судовых двигателях внутреннего сгорания. В отличие от традиционных методов, основанных исключительно на наличии сигнала или пороговых значениях амплитуды, предлагаемый подход использует двухпараметрический анализ, сочетающий временные и частотные характеристики выходного сигнала датчика. В частности, алгоритм непрерывно контролирует два ключевых показателя: длительность отдельных импульсов и количество импульсов, накопленных за фиксированный минутный интервал. Пороговые значения для этих параметров, устанавливаемые в интервале длительности импульсов 50 — 200 мс, определяются на основе рабочего диапазона оборотов двигателя, соответствующего устойчивому холостому ходу и максимальной нагрузке. Отклонения за эти пределы вызывают срабатывание аварийного сигнала, указывающего на потенциальную деградацию датчика, электромагнитные помехи или механические неисправности. Алгоритм реализован в среде CODESYS 2.3 с использованием стандартных функциональных блоков программируемого логического контроллера, что обеспечивает совместимость с существующими системами морской автоматизации и позволяет выполнять бесшовную интеграцию без внесения изменений в аппаратное обеспечение. Вспомогательный модуль моделирования упрощает автономную валидацию, эмулируя реалистичные сигналы датчиков как в номинальных условиях, так и в условиях неисправности. Разработанное решение повышает надежность системы, позволяя своевременно выявлять скрытые неисправности, которые часто не поддаются обнаружению традиционными механизмами самодиагностики, тем самым повышая эксплуатационную безопасность, сокращая время незапланированных простоев и поддерживая стратегии технического обслуживания по состоянию в морских условиях.</p></abstract><trans-abstract xml:lang="en"><p>This paper presents a new diagnostic algorithm for the early detection of faults in the data-measuring channel of inductive speed sensors used in low-speed marine internal combustion engines. Unlike traditional methods based solely on signal presence or amplitude thresholds, the proposed approach involves a two-parameter analysis combining the temporal and frequency characteristics of the sensor output signal. Specifically, the algorithm continuously monitors two key parameters: the duration of individual pulses and the number of pulses accumulated over a fixed oneminute interval. Thresholds for these parameters, set within a pulse duration range of 50 — 200 ms, are determined based on the engine speed operating range, corresponding to stable idle running and maximum load. Deviations beyond these limits trigger an alarm, indicating potential sensor degradation, electromagnetic interference, or mechanical failure. The algorithm is implemented in the CODESYS 2.3 environment using standard programmable logic controller (PLC) function blocks, ensuring compatibility with existing marine automation systems and enabling seamless integration without hardware modifications. The auxiliary simulation module simplifies autonomous validation by emulating realistic sensor signals under both nominal and fault conditions. The developed solution improves system reliability by enabling the timely detection of latent faults that are often undetectable by traditional self-diagnostic mechanisms, thereby enhancing operational safety, reducing unplanned downtime, and supporting condition-based maintenance strategies in marine environments.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>датчик скорости</kwd><kwd>информационно-измерительный канал</kwd><kwd>система управления</kwd><kwd>алгоритм</kwd><kwd>аварийная остановка</kwd><kwd>аналоговый сигнал</kwd><kwd>дискретный сигнал</kwd></kwd-group><kwd-group xml:lang="en"><kwd>speed sensor</kwd><kwd>data-measuring channel</kwd><kwd>control system</kwd><kwd>algorithm</kwd><kwd>emergency stop</kwd><kwd>analog signal</kwd><kwd>discrete signal</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Коновалов П.О. Опыт развития и применения в эксплуатации систем мониторинга технического состояния судовых ДВС / П.О. Коновалов, А.А. Иванченко, Г.Л. Ларионов // Тенденции развития науки и образования. — 2023. — № 100, ч. 5. — С. 102 — 108. — DOI: 10.18411/trnio-08-2023-246. — EDN TWWLBX.</mixed-citation><mixed-citation xml:lang="en">Konovalov P.O., Ivanchenko A.A., Larionov G.L. Experience in the development and application in operation of systems for monitoring the technical condition of marine internal combustion engines. Trends in the development of science and education. 2023. No. 100, pt. 5. P. 102 — 108. DOI 10.18411/trnio-08-2023-246. EDN TWWLBX. (In Russ.)</mixed-citation></citation-alternatives></ref><ref id="cit2"><label>2</label><citation-alternatives><mixed-citation xml:lang="ru">Kang Y.J. Hierarchical level fault detection and diagnosis of ship engine systems / Y.J. Kang, Y.-J. Noh, M.-S. Jang, S. Park, J.-T. Kim // Expert Systems with Applications. — 2023. — Vol. 213. — P. 118814.</mixed-citation><mixed-citation xml:lang="en">Kang Y.J., Noh Y.-J., Jang M.-S., Park S., Kim J.-T. Hierarchical level fault detection and diagnosis of ship engine systems. Expert Systems with Applications. 2023. Vol. 213. P. 118814.</mixed-citation></citation-alternatives></ref><ref id="cit3"><label>3</label><citation-alternatives><mixed-citation xml:lang="ru">Kougiatsos N. A distributed cyber-physical framework for sensor fault diagnosis of marine internal combustion engines / N. Kougiatsos, V. Reppa // IEEE Transactions on Control Systems Technology. — 2024. — Vol. 32, No. 5. — P. 1718 — 1729.</mixed-citation><mixed-citation xml:lang="en">Kougiatsos N., Reppa V. A distributed cyber-physical framework for sensor fault diagnosis of marine internal combustion engines. IEEE Transactions on Control Systems Technology. 2024. Vol. 32, No. 5. P. 1718 — 1729.</mixed-citation></citation-alternatives></ref><ref id="cit4"><label>4</label><citation-alternatives><mixed-citation xml:lang="ru">Orhan M. A literature review and future research agenda on fault detection and diagnosis studies in marine machinery systems / M. Orhan, M. Celik // Proceedings of the Institution of Mechanical Engineers. Part M: Journal of Engineering for the Maritime Environment. — 2024. — Vol. 238, No. 1. — P. 3 — 21.</mixed-citation><mixed-citation xml:lang="en">Orhan M., Celik M. A literature review and future research agenda on fault detection and diagnosis studies in marine machinery systems. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment. 2024. Vol. 238, no. 1. P. 3 — 21.</mixed-citation></citation-alternatives></ref><ref id="cit5"><label>5</label><citation-alternatives><mixed-citation xml:lang="ru">Velasco-Gallego C. RADIS: A real-time anomaly detection intelligent system for fault diagnosis of marine machinery / C. Velasco-Gallego, I. Lazakis // Expert Systems with Applications. — 2022. — Vol. 204. — P. 117634.</mixed-citation><mixed-citation xml:lang="en">Velasco-Gallego C., Lazakis I. RADIS: A real-time anomaly detection intelligent system for fault diagnosis of marine machinery. Expert Systems with Applications. 2022. Vol. 204. P. 117634.</mixed-citation></citation-alternatives></ref><ref id="cit6"><label>6</label><citation-alternatives><mixed-citation xml:lang="ru">Бурков Д.Е. Применение судовой информационной системы для контроля и мониторинга технического состояния судового оборудования / Д.Е. Бурков // Вестник Государственного университета морского и речного флота имени адмирала С.О. Макарова. — 2023. — Т. 15, № 5. — С. 893 — 902.</mixed-citation><mixed-citation xml:lang="en">Burkov D.E. Application of ship’s information system to control and monitor the technical condition of ship's equipment. Vestnik Gosudarstvennogo universiteta morskogo i rechnogo flota imeni admirala S.O. Makarova. 2023. Vol. 15(5). P. 893 — 902. (In Russ.)</mixed-citation></citation-alternatives></ref><ref id="cit7"><label>7</label><citation-alternatives><mixed-citation xml:lang="ru">Жидков Н.А. Повышение экономической и энергетической эффективности судовых двигателей внутреннего сгорания в условиях ужесточения экологических требований / Н.А. Жидков, О.В. Новикова // Цифровая трансформация экономических систем: проблемы и перспективы (ЭКОПРОМ-2022). — СПб.: Политех-пресс, 2022. — С. 274 — 277.</mixed-citation><mixed-citation xml:lang="en">Zhidkov N.A., Novikova O.V. Improving the economic and energy efficiency of marine internal combustion engines under conditions of tightening environmental requirements. Digital Transformation of Economic Systems: Problems and Prospects (ECOPROM-2022). St. Petersburg: Politekh-press, 2022. P. 274 — 277. (In Russ.)</mixed-citation></citation-alternatives></ref><ref id="cit8"><label>8</label><citation-alternatives><mixed-citation xml:lang="ru">Валов Д.С. Системы управления судовыми энергетическими установками автономных судов / Д.С. Валов, С.А. Валгин // Актуальные исследования. — 2023. — № 5 (135). — С. 19 —28.</mixed-citation><mixed-citation xml:lang="en">Valov D.S., Valgin S.A. Control systems for marine power plants of autonomous vessels. Aktual'nye issledovaniya [Current research]. 2023. No. 5 (135). P. 19 — 28.</mixed-citation></citation-alternatives></ref><ref id="cit9"><label>9</label><citation-alternatives><mixed-citation xml:lang="ru">Vrvilo P. Methods and equipment for analysis and diagnosis of marine engines during navigation / P. Vrvilo, T. Vidović, G. Radica, L. Roldo // Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. — 2024. — Vol. 46, No. 1. — P. 15808 — 15824.</mixed-citation><mixed-citation xml:lang="en">Vrvilo P., Vidović T., Radica G., Roldo L. Methods and equipment for analysis and diagnosis of marine engines during navigation. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. 2024. Vol. 46, No. 1. P. 15808 — 15824.</mixed-citation></citation-alternatives></ref><ref id="cit10"><label>10</label><citation-alternatives><mixed-citation xml:lang="ru">Pagonis D.N. Sensors and measurement systems for marine engineering applications / D.N. Pagonis // Applied Sciences. — 2024. — Vol. 14, No. 9. — P. 3761.</mixed-citation><mixed-citation xml:lang="en">Pagonis D.N. Sensors and measurement systems for marine engineering applications. Applied Sciences. 2024. Vol. 14, No. 9. P. 3761.</mixed-citation></citation-alternatives></ref><ref id="cit11"><label>11</label><citation-alternatives><mixed-citation xml:lang="ru">Kim D. Explainable anomaly detection framework for maritime main engine sensor data / D. Kim, G. Antariksa, M.P. Handayani, S. Lee, J. Lee // Sensors. — 2021. — Т. 21, No. 15. — P. 5200.</mixed-citation><mixed-citation xml:lang="en">Kim D., Antariksa G., Handayani M.P., Lee S., Lee J. Explainable anomaly detection framework for maritime main engine sensor data. Sensors. 2021. Vol. 21, No. 15. P. 5200.</mixed-citation></citation-alternatives></ref><ref id="cit12"><label>12</label><citation-alternatives><mixed-citation xml:lang="ru">Liu B. Research on fault early warning of marine diesel engine based on CNN-BiGRU / B. Liu, H. Gan, D. Chen, Z. Shu // Journal of Marine Science and Engineering. — 2022. — Vol. 11, No. 1. — P. 56.</mixed-citation><mixed-citation xml:lang="en">Liu B., Gan H., Chen D., Shu Z. Research on fault early warning of marine diesel engine based on CNN-BiGRU. Journal of Marine Science and Engineering. 2022. Vol. 11, No. 1. P. 56.</mixed-citation></citation-alternatives></ref><ref id="cit13"><label>13</label><citation-alternatives><mixed-citation xml:lang="ru">Jovanović I. Combined fault tree analysis and Bayesian network for reliability assessment of marine internal combustion engine / I. Jovanović, Ç. Karatuğ, M. Perčić, N. Vladimir // Journal of Marine Science and Application. — 2025. — DOI: 10.1007/s11804-025-00692-7.</mixed-citation><mixed-citation xml:lang="en">Jovanović I., Karatuğ Ç., Perčić M., Vladimir N. Combined fault tree analysis and Bayesian network for reliability assessment of marine internal combustion engine. Journal of Marine Science and Application. 2025. DOI: 10.1007/s11804-025-00692-7.</mixed-citation></citation-alternatives></ref><ref id="cit14"><label>14</label><citation-alternatives><mixed-citation xml:lang="ru">Yaqin R.I. Failure analysis of fuel system main engine fishing vessel (case study: KM. Sumber Mutiara) / R.I. Yaqin, M. Akmal, J.P. Siahaan, M.L. Umar et al. // Kapal: Jurnal Ilmu Pengetahuan dan Teknologi Kelautan. — 2023. — Vol. 20, No. 1. — P. 34 — 43.</mixed-citation><mixed-citation xml:lang="en">Yaqin R.I., Akmal M., Siahaan J.P., Umar M.L. et al. Failure analysis of fuel system main engine fishing vessel (case study: KM. Sumber Mutiara). Kapal: Jurnal Ilmu Pengetahuan dan Teknologi Kelautan. 2023. Vol. 20, No. 1. P. 34 — 43.</mixed-citation></citation-alternatives></ref><ref id="cit15"><label>15</label><citation-alternatives><mixed-citation xml:lang="ru">Jan S.U. A distributed sensor-fault detection and diagnosis framework using machine learning / S.U. Jan, Y.D. Lee, I.S. Koo // Information Sciences. — 2021. — Vol. 547. — P. 777 — 796.</mixed-citation><mixed-citation xml:lang="en">Jan S.U., Lee Y.D., Koo I.S. A distributed sensor-fault detection and diagnosis framework using machine learning. Information Sciences. 2021. Vol. 547. P. 777 — 796.</mixed-citation></citation-alternatives></ref></ref-list><fn-group><fn fn-type="conflict"><p>The authors declare that there are no conflicts of interest present.</p></fn></fn-group></back></article>
