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<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">pribor</journal-id><journal-title-group><journal-title xml:lang="ru">Известия высших учебных заведений. Приборостроение</journal-title><trans-title-group xml:lang="en"><trans-title>Journal of Instrument Engineering</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">0021-3454</issn><issn pub-type="epub">2500-0381</issn><publisher><publisher-name>Национальный исследовательский университет ИТМО</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.17586/0021-3454-2025-68-9-762-773</article-id><article-id custom-type="elpub" pub-id-type="custom">pribor-407</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>СИСТЕМНЫЙ АНАЛИЗ, УПРАВЛЕНИЕ И ОБРАБОТКА ИНФОРМАЦИИ</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>SYSTEM ANALYSIS, MANAGEMENT AND INFORMATION PROCESSING</subject></subj-group></article-categories><title-group><article-title>Обнаружение отказов датчиков автономного подводного аппарата с использованием машинного обучения и генераторов рассогласования</article-title><trans-title-group xml:lang="en"><trans-title>Autonomous Underwater Vehicle Sensor Fault Detection Using Machine Learning and Mismatch Generators</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>Базылев</surname><given-names>Д. Н.</given-names></name><name name-style="western" xml:lang="en"><surname>Bazylev</surname><given-names>D. N.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Дмитрий Николаевич Базылев — канд. техн. наук; доцент; факультет систем управления и робототехники</p><p>Санкт-Петербург</p></bio><bio xml:lang="en"><p>Dmitry N. Bazylev — PhD; Faculty of Control Systems and Robotics; Associate Professor</p><p>St. Petersburg</p></bio><email xlink:type="simple">bazylevd@itmo.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>Маргун</surname><given-names>А. А.</given-names></name><name name-style="western" xml:lang="en"><surname>Margun</surname><given-names>A. A.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Алексей Анатольевич Маргун — канд. техн. наук; доцент; факультет систем управления и робототехники</p><p>Санкт-Петербург</p></bio><bio xml:lang="en"><p>Alexey A. Margun — PhD; Faculty of Control Systems and Robotics; Associate Professor</p><p>St. Petersburg</p></bio><email xlink:type="simple">aamargun@itmo.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>Галкина</surname><given-names>Д. А.</given-names></name><name name-style="western" xml:lang="en"><surname>Galkina</surname><given-names>D. A.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Дарья Алексеевна Галкина — аспирант; факультет систем управления и робототехники</p><p>Санкт-Петербург</p><p> </p></bio><bio xml:lang="en"><p>Daria A. Galkina — Post-Graduate Student; Faculty of Control Systems and Robotics</p><p>St. Petersburg</p></bio><email xlink:type="simple">da.galkina@itmo.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>Ляховский</surname><given-names>Н. В.</given-names></name><name name-style="western" xml:lang="en"><surname>Lyahovsky</surname><given-names>M. V.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Максим Вадимович Ляховский — аспирант; факультет систем управления и робототехники</p><p>Санкт-Петербург</p></bio><bio xml:lang="en"><p>Maxim V. Lyahovsky — Post-Graduate Student; Faculty of Control Systems and Robotics</p><p>St. Petersburg</p></bio><email xlink:type="simple">maxim.lyahovsky@gmail.com</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Университет ИТМО</institution></aff><aff xml:lang="en"><institution>ITMO University</institution></aff></aff-alternatives><pub-date pub-type="collection"><year>2025</year></pub-date><pub-date pub-type="epub"><day>29</day><month>10</month><year>2025</year></pub-date><volume>68</volume><issue>9</issue><fpage>762</fpage><lpage>773</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Национальный исследовательский университет ИТМО, 2025</copyright-statement><copyright-year>2025</copyright-year><copyright-holder xml:lang="ru">Национальный исследовательский университет ИТМО</copyright-holder><copyright-holder xml:lang="en">Национальный исследовательский университет ИТМО</copyright-holder><license xlink:href="https://pribor.ifmo.ru/jour/about/submissions#copyrightNotice" xlink:type="simple"><license-p>https://pribor.ifmo.ru/jour/about/submissions#copyrightNotice</license-p></license></permissions><self-uri xlink:href="https://pribor.ifmo.ru/jour/article/view/407">https://pribor.ifmo.ru/jour/article/view/407</self-uri><abstract><p>Рассматривается задача обнаружения и локализации отказов датчиков инерциальной системы, используемых автономным подводным аппаратом при навигации. Предложен сравнительный анализ решений, которые базируются на различных моделях машинного обучения. На базе наблюдателей полного порядка построены направленные генераторы сигналов рассогласований для обнаружения и локализации отказов датчиков, используемых для измерения линейной и угловой скорости модели подводного аппарата. Рассмотрена динамика модели автономного подводного аппарата, движущегося с постоянной продольной скоростью в горизонтальной плоскости. Сформулированы условия, обеспечивающие корректное обнаружение отказов и чувствительность генераторов рассогласования к отказу отдельного датчика. Спроектированы функции, используемые в качестве признаков для предложенных методов машинного обучения. Выполнена настройка и проведен сравнительный анализ эффективности различных моделей машинного обучения в задаче диагностирования датчиков. Приведены результаты компьютерного моделирования, демонстрирующие высокую точность определения отказов при аугментации данных для обучения за счет сигналов рассогласования.</p></abstract><trans-abstract xml:lang="en"><p>The problem of fault detecting and localizing of inertial system sensors used by an autonomous underwater vehicle during navigation is considered. A comparative analysis of solutions based on various machine learning models is proposed. Directional mismatch signal generators are built on the basis of full-order observers to detect and localize failures of sensors used to measure the linear and angular velocity of an underwater vehicle model. The dynamics of the autonomous underwater vehicle model moving at a constant longitudinal velocity in a horizontal plane is considered. The conditions are formulated to ensure the correct detection of failures and the sensitivity of mismatch generators to the failure of an individual sensor. The functions used as features for the proposed machine learning methods are designed. The setup was performed and a comparative analysis of the effectiveness of various machine learning models in the task of diagnosing sensors are carried out. The results of computer modeling are presented, demonstrating the high accuracy of fault detection during data augmentation for training due to mismatch signals.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>автономный подводный аппарат</kwd><kwd>обнаружение и локализация отказов</kwd><kwd>наблюдатель полного порядка</kwd><kwd>методы машинного обучения</kwd><kwd>аугментация данных</kwd></kwd-group><kwd-group xml:lang="en"><kwd>autonomous underwater vehicle</kwd><kwd>fault detection and localization</kwd><kwd>full-order observer</kwd><kwd>machine learning methods</kwd></kwd-group><funding-group><funding-statement xml:lang="ru">Статья подготовлена при финансовой поддержке Министерства науки и высшего образования Российской Федерации, проект № FSER-2025-0002.</funding-statement><funding-statement xml:lang="en">The article was prepared with the financial support of the Ministry of Science and Higher Education of the Russian Federation, project No. FSER-2025-0002.</funding-statement></funding-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Babaei M., Shi J., Abdelwahed S. 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