Діагностика та прогнозне обслуговування індустріальних електродвигунів
Automatic continuous or periodic control is the most promising method of diagnosing electric motors in production nowadays. It is aimed at predicting breakdowns and the remaining useful lifetime of motors. However, research in this area remains purely theoretical and eitherfocuses on very narrow pro...
Збережено в:
| Дата: | 2023 |
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| Автори: | , , , |
| Формат: | Стаття |
| Мова: | Українська |
| Опубліковано: |
Інститут прикладних проблем механіки і математики ім. Я. С. Підстригача НАН України
2023
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| Теми: | |
| Онлайн доступ: | https://www.fmmit.lviv.ua/index.php/fmmit/article/view/341 |
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| Назва журналу: | Physico-mathematical modeling and informational technologies |
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Репозитарії
Physico-mathematical modeling and informational technologies| Резюме: | Automatic continuous or periodic control is the most promising method of diagnosing electric motors in production nowadays. It is aimed at predicting breakdowns and the remaining useful lifetime of motors. However, research in this area remains purely theoretical and eitherfocuses on very narrow problems or provides too superficial overview. Consequently,manual control devices or partially automated devices are mainly used in practice [1]. In view of this, there is great interest in the development of a software method for predictive maintenance of electric motors. In this research recurrent neural networks with long short-term memory layers (LSTM-layers) are investigated due to their ability to effectively model sequential data and learn complex dependencies [2]. |
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| DOI: | 10.15407/fmmit2023.38.081 |