Citation: Xiang, G.; Miao, J.; Cui, L.;
Hu, X. Intelligent Fault Diagnosis for
Inertial Measurement Unit through
Deep Residual Convolutional Neural
Network and Short-Time Fourier
Transform. Machines 2022, 10, 851.
https://doi.org/10.3390/
machines10100851
Academic Editors: Chris Zhang,
Kelvin K.L. Wong, Dhanjoo N. Ghista
and Andrew W.H. Ip
Received: 17 August 2022
Accepted: 20 September 2022
Published: 23 September 2022
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Article
Intelligent Fault Diagnosis for Inertial Measurement Unit
through Deep Residual Convolutional Neural Network and
Short-Time Fourier Transform
Gang Xiang
1,2,
* , Jing Miao
3
, Langfu Cui
1
and Xiaoguang Hu
1
1
School of Automation and Electrical Engineering, Beijing University of Aeronautics and Astronautics,
Beijing 100191, China
2
Beijing Aerospace Automatic Control Institute, Beijing 100040, China
3
Beijing Institute of Electronic System Engineer, Beijing 100854, China
* Correspondence: xianggang@buaa.edu.cn; Tel.: +86-10-88525913
Abstract:
An Inertial Measurement Unit (IMU) is a significant component of a spacecraft, and its fault
diagnosis results directly affect the spacecraft’s stability and reliability. In recent years, deep learning-
based fault diagnosis methods have made great achievements; however, some problems such as
how to extract effective fault features and how to promote the training process of deep networks are
still to be solved. Therefore, in this study, a novel intelligent fault diagnosis approach combining a
deep residual convolutional neural network (CNN) and a data preprocessing algorithm is proposed.
Firstly, the short-time Fourier transform (STFT) is adopted to transform the raw time domain data
into time–frequency images so the useful information and features can be extracted. Then, the Z-score
normalization and data augmentation strategies are both explored and exploited to facilitate the
training of the subsequent deep model. Furthermore, a modified CNN-based deep diagnosis model,
which utilizes the Parameter Rectified Linear Unit (PReLU) as activation functions and residual
blocks, automatically learns fault features and classifies fault types. Finally, the experiment’s results
indicate that the proposed method has good fault features’ extraction ability and performs better
than other baseline models in terms of classification accuracy.
Keywords: IMU; deep learning; residual network; fault diagnosis
1. Introduction
Inertial Measurement Units (IMUs), which usually contain several sophisticated in-
ertial sensors such as gyroscopes and accelerometers, are the essential components of
spacecraft, e.g., satellites and launch vehicles [
1
]. IMUs can not only measure the three-axis
angular velocity as well as acceleration, but also autonomously establish the azimuth
and attitude reference of spacecraft under various complex environmental conditions [
2
].
Moreover, IMUs give the posture and position information of spacecraft and play a critical
role in providing feedback to the on-board controller. Thus, an IMU is directly relevant to
the performance of a spacecraft.
In order to monitor the working state and enhance the stability of IMUs, several fault
diagnosis methods have been proposed by researchers [
3
]. However, it is not appropriate
to conduct fault diagnosis directly in the outer space environment due to the fact that the
spacecraft is usually complex and usually has limited computation resources. At present,
one common fault diagnosis method is to mine telemetry data in the ground center. The
telemetry data measuring the status of in-orbit spacecraft are mainly produced by sensors
of IMUs and then transmitted to the ground telemetry center.
The traditional fault diagnosis procedure involves artificial feature extraction and fault
mode classification. The artificial feature extraction using signal processing algorithms
Machines 2022, 10, 851. https://doi.org/10.3390/machines10100851 https://www.mdpi.com/journal/machines
