Citation: Liu, X.; Teng, W.; Liu, Y. A
Model-Agnostic Meta-Baseline
Method for Few-Shot Fault Diagnosis
of Wind Turbines. Sensors 2022, 22,
3288. https://doi.org/10.3390/
s22093288
Academic Editors: Wenjun
(Chris) Zhang, Dhanjoo N. Ghista,
Kelvin K.L. Wong and Andrew
W.H. Ip
Received: 15 March 2022
Accepted: 19 April 2022
Published: 25 April 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
A Model-Agnostic Meta-Baseline Method for Few-Shot Fault
Diagnosis of Wind Turbines
Xiaobo Liu
1
, Wei Teng
1,2
and Yibing Liu
1,2,
*
1
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.);
tengw@ncepu.edu.cn (W.T.)
2
Hebei Key Laboratory of Electric Machinery Health Maintenance & Failure Prevention, North China Electric
Power University, Baoding 071003, China
* Correspondence: lyb@ncepu.edu.cn
Abstract:
The technology of fault diagnosis is helpful to improve the reliability of wind turbines,
and further reduce the operation and maintenance cost at wind farms. However, in reality, wind
turbines are not allowed to operate with faults, so few fault samples could be obtained. With a
small amount of training data, traditional fault diagnosis models that need huge samples under a
deep learning framework are difficult to maintain with high accuracy and effectiveness. Few-shot
learning can effectively solve the problem of overfitting caused by fewer fault samples in model
training. In view of model-agnostic meta-learning (MAML), this paper proposes a model for few-
shot fault diagnosis of the wind turbines drivetrain, which is named model-agnostic meta-baseline
(MAMB). The training data is input to the base classification model for pre-training, then, some data
is randomly selected from the training set to form multiple meta-learning tasks that are utilized
to train the MAML to finally fine-tune the later layers of the model at a smaller learning rate. The
proposed model was analyzed by the small samples of the bearing data from Case Western Reserve
University (CWRU) data, the generator bearings, and gearboxes vibration data in wind turbines
under randomly changing operating conditions. The results verified that the proposed method was
superior in one-shot, five-shot, and ten-shot tasks of wind turbines.
Keywords: few-shot learning; fault diagnosis; model-agnostic meta-learning; wind turbines
1. Introduction
As the installed capacity of wind turbines increases rapidly, the technology of condi-
tion monitoring and fault diagnosis attracts more attention to guarantee the operational
reliability of wind turbines. The huge vibration data collected from wind farms prompts the
development of intelligent diagnosis of wind turbines, which is driven by the progress of
the technology of artificial intelligence. However, in reality, wind turbines are not allowed
to run with faults. When a fault occurs, the wind turbine has to shut down. Therefore, the
collected operating data are mostly normal data under healthy status, with very few fault
data. Obviously, these kinds of data from wind turbines are insufficient to train an intelli-
gent classification model using traditional deep learning, due to the potential overfitting
caused by sample imbalance and type imbalance. Data augmentation and regularization
techniques can alleviate the overfitting caused by low data volume [
1
]. Data augmentation
refers to the addition of data by manual rules such as pan, flip, cut, and rotate. Designing
these rules relies heavily on domain knowledge and requires expensive labor costs. Reg-
ularization can be used to correct the direction of descent. However, neither of the two
methods can fundamentally solve the overfitting problem when data are extremely scarce.
Few-shot learning can train deep models with very limited data and solve the problem
of overfitting caused by a small number of fault samples. The current few-shot learning
makes full use of the advantages of deep neural networks in feature representation and
Sensors 2022, 22, 3288. https://doi.org/10.3390/s22093288 https://www.mdpi.com/journal/sensors
