Citation: Liu, X.; Ma, H.; Liu, Y. A
Novel Transfer Learning Method
Based on Conditional Variational
Generative Adversarial Networks for
Fault Diagnosis of Wind Turbine
Gearboxes under Variable Working
Conditions. Sustainability 2022, 14,
5441. https://doi.org/10.3390/
su14095441
Academic Editors: Luis
Hernández-Callejo,
Sergio Nesmachnow and
Sara Gallardo Saavedra
Received: 26 March 2022
Accepted: 29 April 2022
Published: 30 April 2022
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Article
A Novel Transfer Learning Method Based on Conditional
Variational Generative Adversarial Networks for Fault
Diagnosis of Wind Turbine Gearboxes under Variable
Working Conditions
Xiaobo Liu, Haifei Ma and Yibing Liu *
Key Laboratory of Power Station Energy Transfer Conversion and System, North China Electric Power University,
Ministry of Education, Beijing 102206, China; liuxiaobo116@163.com (X.L.); mahaifeii@163.com (H.M.)
* Correspondence: lyb@ncepu.edu.cn
Abstract:
The rapid development of artificial intelligence offers more opportunities for intelligent
mechanical diagnosis. Recently, due to various reasons such as difficulty in obtaining fault data
and random changes in operating conditions, deep transfer learning has achieved great attention in
solving mechanical fault diagnoses. In order to solve the problems of variable working conditions
and data imbalance, a novel transfer learning method based on conditional variational generative
adversarial networks (CVAE-GAN) is proposed to realize the fault diagnosis of wind turbine test bed
data. Specifically, frequency spectra are employed as model signals, then the improved CVAE-GAN
are implemented to generate missing data for other operating conditions. In order to reduce the
difference in distribution between the source and target domains, the maximum mean difference
(MMD) is used in the model to constrain the training of the target domain generation model. The
generated data is used to supplement the missing sample data for fault classification. The verification
results confirm that the proposed method is a promising tool that can obtain higher diagnosis
efficiency. The feature embedding is visualized by t-distributed stochastic neighbor embedding
(t-SNE) to test the effectiveness of the proposed model.
Keywords:
conditional variational generative adversarial networks; transfer learning; wind turbines;
variable working conditions
1. Introduction
Fault diagnosis of wind turbines plays an important role in equipment health man-
agement. Recently, deep learning (DL) has become a promising method in intelligent fault
diagnosis. DL methods usually follow two principles: (1) the dataset should be large and
well labeled and (2) the training and testing datasets are subject to the same distribution.
However, in reality, wind turbines often face the problems of working condition variation,
sample imbalance, and few fault samples, which brings challenges for deep learning to
achieve wind turbine fault diagnosis. Compared with DL, transfer learning (TL) allows
different probability distributions of samples between source and target domains. This
means that a new but related task in the target domain can be effectively addressed by the
learned knowledge from the source domain.
TL-based models have been employed for intelligent fault diagnosis under different
working conditions. Li et al. proposed a novel weighted adversarial transfer network
(WATN) for partial domain fault diagnosis [
1
]. Huang et al. proposed a deep adversar-
ial capsule network (DACN) to embed multi-domain generalization into the intelligent
compound fault diagnosis [
2
]. Li et al. proposed a two-stage transfer adversarial network
(TSTAN) for multiple new faults detection of rotating machinery [
3
]. Chen et al. proposed
a transferable convolutional neural network to improve the learning of target tasks [
4
].
Sustainability 2022, 14, 5441. https://doi.org/10.3390/su14095441 https://www.mdpi.com/journal/sustainability
