vrgupВестник Ростовского государственного университета путей сообщенияVestnik Rostovskogo gosudarstvennogo universiteta putej soobŝeniâ0201-727XРостовский государственный университет путей сообщения10.46973/0201-727X_2026_2_194vrgup-344Research ArticleМАШИНОСТРОЕНИЕMECHANICAL ENGINEERINGОценка остаточного ресурса конструкций при случайном нагружении на основе одномерной свёрточной нейронной сетиResidual life assessment of structures under random loading based on a one-dimensional convolutional neural networkЕрпаловА. В.ErpalovA. V.

Ерпалов Алексей Викторович, старший научный сотрудник, кандидат технических наук

Erpalov Aleksey Victorovich, Senior Researcher, Candidate of Engineering Sciences

erpalovav@susu.ruХорошевскийК. А.KhoroshevskyK. A.

Хорошевский Кирилл Антонович, инженер

Khoroshevsky Kirill Antonovich, Engineer

khoroshevskiika@susu.ruГадолинаИ. В.GadolinaI. V.

Гадолина Ирина Викторовна, старший научный сотрудник, кандидат технических наук, доцент

Gadolina Irina Viсtorovna, Senior Researcher, Candidate of Engineering Sciences, Associate Professor

gadolina@mail.ruЮжно-Уральский государственный университет (национальный исследовательский университет) (ФГАОУ ВО «ЮУрГУ (НИУ)»)РоссияSouth Ural State University (National Research University) (FSAEIHE SUSU (NRU))Russian FederationИнститут машиноведения им. А. А. Благонравова Российской академии наукРоссияInstitute of Machines Sciense named after A. A. Blagonravov of the Russian Academy of Sciences (IMASH RAS)Russian Federation20262907202602194–209Copyright © Ерпалов А.В., Хорошевский К.А., Гадолина И.В., 20262026Ерпалов А.В., Хорошевский К.А., Гадолина И.В.Erpalov A.V., Khoroshevsky K.A., Gadolina I.V.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vestnik.rgups.ru/jour/article/view/344

Предложена модель оценки остаточного ресурса элементов конструкций при случайном вибрационном нагружении на основе одномерной свёрточной нейронной сети (1D-CNN), на вход которой подаётся распределение максимумов процесса напряжений, а на выходе формируется относительная долговечность. Для обучения сети сформирован набор из 12 000 реализаций стационарных и нестационарных случайных процессов, включая процессы с ударными воздействиями и реальные эксплуатационные записи; эталонные значения долговечности получены методом «падающего дождя» с линейным суммированием повреждений, а гиперпараметры оптимизированы байесовским методом (минимальная ошибка RMSE = 0,0403). Экспериментальная верификация на образцах из лёгкого сплава МА-15, испытанных на электродинамическом вибростенде при трёх уровнях нагружения, показала погрешность прогноза от 6 до 25 %, возрастающую при высоких уровнях вследствие малоцикловой усталости. С учётом разброса экспериментальных данных модель признана верифицированной.

A model is proposed for assessing the residual life of structural elements under random vibration loading, based on a one-dimensional convolutional neural network (1D-CNN) that takes the stress-process maxima distribution as input and outputs the relative durability. A training set of 12,000 realizations of stationary and non-stationary random processes – including processes with shock impacts and real operational records – was generated; the reference durability values were obtained by the rainflow method with linear damage summation, and the hyperparameters were tuned by Bayesian optimization (minimum error RMSE = 0.0403). Experimental verification on MA-15 light-alloy specimens tested on an electrodynamic shaker at three loading levels yielded a prediction error of 6 to 25 %, increasing at high levels owing to low-cycle fatigue. Taking the scatter of the experimental data into account, the model is considered verified.

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The authors declare that there are no conflicts of interest present.