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

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

Subject

Multidisciplinary

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