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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">sustain</journal-id><journal-title-group><journal-title xml:lang="ru">Надежность</journal-title><trans-title-group xml:lang="en"><trans-title>Dependability</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">1729-2646</issn><issn pub-type="epub">2500-3909</issn><publisher><publisher-name>RAMS Journal Limited liability company</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.21683/1729-2646-2022-22-3-3-10</article-id><article-id custom-type="elpub" pub-id-type="custom">sustain-478</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 В ЗАДАЧАХ НАДЕЖНОСТИ И БЕЗОПАСНОСТИ</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>SYSTEM ANALYSIS IN DEPENDABILITY AND SAFETY</subject></subj-group></article-categories><title-group><article-title>Применение теории нечетких данных в задачах оценки долговечности</article-title><trans-title-group xml:lang="en"><trans-title>Application of the fuzzy data theory in durability estimation</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>Гадолина</surname><given-names>И. B.</given-names></name><name name-style="western" xml:lang="en"><surname>Gadolina</surname><given-names>I.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Гадолина Ирина Викторовна – кандидат технических наук, доцент, старший научный сотрудник</p><p>Москва</p></bio><bio xml:lang="en"><p>Irina V. Gadolina, Candidate of Engineering, Associate Professor, Senior Researcher</p><p>Moscow</p></bio><email xlink:type="simple">gadolina@mail.ru</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Федеральное государственное бюджетное учреждение науки Институт машиноведения им. А.А. Благонравова Российской академии наук</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Mechanical Engineering Research Institute of the Russian Academy of Sciences</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2022</year></pub-date><pub-date pub-type="epub"><day>28</day><month>09</month><year>2022</year></pub-date><volume>22</volume><issue>3</issue><fpage>3</fpage><lpage>10</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Гадолина И.B., 2022</copyright-statement><copyright-year>2022</copyright-year><copyright-holder xml:lang="ru">Гадолина И.B.</copyright-holder><copyright-holder xml:lang="en">Gadolina I.</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://www.dependability.ru/jour/article/view/478">https://www.dependability.ru/jour/article/view/478</self-uri><abstract><sec><title>Цель</title><p>Цель. Задачи в исследованиях усталости материалов, являясь актуальными с точки зрения инженерной практики, обладают большой долей неопределенности. Кривая усталости содержит цензурирования (что означает прошедшие запланированный цикл нагружений и не разрушившиеся к концу испытаний образцы), а блок нагружения, составляемый для расчета долговечности, может быть составлен по-разному с достаточной долей субъективных решений. Блок нагружения предназначен для расчета долговечности и для составления плана испытаний и должен в полной мере отражать всю предполагаемую историю эксплуатации. Оба этих фактора рассматриваются в статье как элементы нечеткой логики. Рассмотрено создание научно обоснованного блока нагружения, который учитывал бы возможные режимы работы в правильной пропорции и с учетом вариабельности. Это связано с тем, что усталостное повреждение накапливается в течение всего срока работы машины и должно быть научно оценено для адекватной оценки в вероятностном аспекте. </p></sec><sec><title>Методы</title><p>Методы. Так как режимы эксплуатации некоторой конкретной детали точно не определены (и не могут быть определены по логике случайного использования машин), рассматриваются проекции случайных нечетких распределений. Удалось научно обосновано учесть конечное множество эксплуатационных режимов в их разумной пропорции. На примере анализа нагружения ответственной детали подвижного состава построены распределения и оценено возможное распределение ресурса детали. Полученные на основе разработанного метода результаты позволят оценить риски эксплуатации и спрогнозировать потребное число запчастей. Рассмотрение цензурированных элементов выборки при построении кривой усталости позволит сделать оценку параметров кривой усталости более состоятельной. </p></sec><sec><title>Выводы</title><p>Выводы. Применение нечетких множеств может оказаться весьма полезным при рассмотрении кривой усталости и при оценке вариации долговечности. Показаны примеры применения предложенного подхода.</p></sec></abstract><trans-abstract xml:lang="en"><sec><title>Aim</title><p>Aim. Problems associated with the study of material fatigue, while being relevant in terms of engineering practice, have a significant degree of uncertainty. The fatigue curve is censored (which indicates the presence of items that have passed the planned load cycle and destroyed by the end of the tests), while the load block made for calculating durability can be designed in a number of ways with a sufficient share of subjective decisions. The load block is intended for calculating durability and defining test plans. It is to fully reflect the entire expected operational history. Both factors are considered in the paper as elements of fuzzy logic. The author examines the creation of a scientifically substantiated load block that would take into account the possible operating modes in a right proportion and taking into account the variability. That is due to the fact that fatigue damage accumulates over the entire life of a machine and is to be scientifically evaluated for an adequate probabilistic assessment. </p></sec><sec><title>Methods</title><p>Methods. As the modes of operation of a certain part are not precisely defined (and cannot be defined by virtue of the logic of random use of machines), projections of random fuzzy distributions are considered. A finite set of operating modes in a reasonable proportion was successfully scientifically substantiated. Using the example of load analysis of a critical part of rolling stock, distributions were constructed and the possible distribution of a part’s life was estimated. The output of the developed method will allow assessing the operational risks and predict the required number of spare parts. By taking into account the censored sample elements in the process of fatigue curve construction, the estimation of the fatigue curve parameters can be made more consistent. </p></sec><sec><title>Conclusions</title><p>Conclusions. The use of fuzzy sets may prove to be very useful when examining fatigue curves and estimating durability variation. Examples are given of applying the proposed method.</p></sec></trans-abstract><kwd-group xml:lang="ru"><kwd>нечеткие множества</kwd><kwd>оценка долговечности</kwd><kwd>режимы нагружения</kwd><kwd>кластерный анализ</kwd><kwd>временные ряды</kwd></kwd-group><kwd-group xml:lang="en"><kwd>fuzzy sets</kwd><kwd>durability estimation</kwd><kwd>loading modes</kwd><kwd>cluster analysis</kwd><kwd>time series.</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">Орлов А.И. Искусственный интеллект: нечисловая статистика: учебник. М.: Ай Пи Ар Медиа, 2022. 446 c. URL: https://www.iprbookshop.ru/117028.html</mixed-citation><mixed-citation xml:lang="en">. Orlov A.I. [Artificial Intelligence: Non-numerical Statistics]. Moscow: IPR Media; 2022. 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