| Abstract: |
| The planetary gearbox is a critical part of wind turbines, and has great significance for their safety and reliability.
Intelligent fault diagnosis methods for these gearboxes have made some achievements based on the availability of
large quantities of labeled data. However, the data collected from the diagnosed devices are always unlabeled, and
the acquisition of fault data from real gearboxes is time-consuming and laborious. As some gearbox faults can be
conveniently simulated by a relatively precise dynamic model, the data from dynamic simulation containing some
features are related to those from the actual machines. As a potential tool, transfer learning adapts a network trained
in a source domain to its application in a target domain. Therefore, a novel fault diagnosis method combining transfer
learning with dynamic model is proposed to identify the health conditions of planetary gearboxes. In the method, a
modified lumped-parameter dynamic model of a planetary gear train is established to simulate the resultant vibration
signal, while an optimized deep transfer learning network based on a one-dimensional convolutional neural network
is built to extract domain-invariant features from different domains to achieve fault classification. Various groups of
transfer diagnosis experiments of planetary gearboxes are carried out, and the experimental results demonstrate the
effectiveness and the reliability of both the dynamic model and the proposed method. |
| Key words: Wind turbine planetary gearbox, Lumped-parameter dynamic model, Intelligent fault diagnosis,
Convolutional neural network, Transfer learning theory |
| DOI：10.1186/s41601-022-00244-z |
|
| Fund:Natural Science Foundation of Shanghai (21ZR1425400), Shanghai Rising
Star Program (21QC1400200), National Natural Science Foundation of China
(51977128), Shanghai Science and Technology Project (20142202600). |
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