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

• Published in: Scientific reports Vol. 12; no. 1; p. 17023
• Main Authors: Song, Meng-Meng, Xiong, Zi-Cheng, Zhong, Jian-Hua, Xiao, Shun-Gen, Tang, Yao-Hong
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 11.10.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/987; 639/166/988; Adaptation; Artificial intelligence; Fault diagnosis; Humanities and Social Sciences; multidisciplinary; Neural networks; Science; Science (multidisciplinary); Simulation; Transfer learning
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-21339-5

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.