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Research on fault diagnosis method of planetary gearbox based on dynamic simulation and deep transfer learning

التفاصيل البيبلوغرافية
العنوان: Research on fault diagnosis method of planetary gearbox based on dynamic simulation and deep transfer learning
المؤلفون: Meng-Meng Song, Zi-Cheng Xiong, Jian-Hua Zhong, Shun-Gen Xiao, Yao-Hong Tang
المصدر: Scientific Reports, Vol 12, Iss 1, Pp 1-15 (2022)
بيانات النشر: Nature Portfolio, 2022.
سنة النشر: 2022
المجموعة: LCC:Medicine
LCC:Science
مصطلحات موضوعية: Medicine, Science
الوصف: Abstract To address the issue of not having enough labeled fault data for planetary gearboxes in actual production, this research develops a simulation data-driven deep transfer learning fault diagnosis method that applies fault diagnosis knowledge from a dynamic simulation model to an actual planetary gearbox. Massive amounts of different fault simulation data are collected by creating a dynamic simulation model of a planetary gearbox. A fresh deep transfer learning network model is built by fusing one-dimensional convolutional neural networks, attention mechanisms, and domain adaptation methods. The network model is used to learn domain invariant features from simulated data, thereby enabling fault diagnosis on real data. The fault diagnosis experiment is verified by using the Drivetrain Diagnostics Simulator test bench. The validity of the proposed means is evaluated by comparing the diagnostic accuracy of various means on various diagnostic tasks.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2045-2322
Relation: https://doaj.org/toc/2045-2322
DOI: 10.1038/s41598-022-21339-5
URL الوصول: https://doaj.org/article/e4bd4d1deae44762b1468b15e26b8b4f
رقم الأكسشن: edsdoj.4bd4d1deae44762b1468b15e26b8b4f
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20452322
DOI:10.1038/s41598-022-21339-5