Author:
Song Meng-Meng,Xiong Zi-Cheng,Zhong Jian-Hua,Xiao Shun-Gen,Tang Yao-Hong
Abstract
AbstractTo 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.
Funder
Natural Science Foundation of Fujian Province
Key Technology Innovation Project of Fujian Province
Youth and Middle-aged Science and Technology Project of Ningde Normal University
Innovation Team of Ningde Normal University
Publisher
Springer Science and Business Media LLC
Cited by
7 articles.
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