Chapter 47
Bearing Fault Transfer Diagnosis Based
on Acoustic Emission Signal Under
Variable Working Conditions
Jiwang Zhang, Keqin Ding, Hongzhu Wang, and Guang Chen
Abstract In recent years, the intelligent diagnosis of bearing fault under stable
condition based on machine learning and vibration signal has made a lot of achievements. However, the performance degradation of these methods is obvious under
variable working conditions, which makes the intelligent diagnosis of bearing fault
still a challenge. In order to solve this problem, a rolling bearing fault transfer diagnosis method based on acoustic emission signal is proposed in this paper. The AE
data under different working conditions are collected and converted to signal map
firstly, then the sensitive features are extracted and the labeled knowledge in source
domain is transferred to the without labeled target domain by using convolutional
neural network and domain adaptation. Finally, the feasibility and superiority of the
proposed method was verified by lots of bearing experiments, and the classification
accuracy reached 88.40%, which is much higher than the traditional methods.
47.1 Introduction
Rolling bearing is the key component of rotating machinery equipment. It mainly
bears the weight and working load of the rotating body and ensures the rotating
accuracy of the rotating body [1]. Therefore, it is of great significance to monitor
and diagnose its health condition [2–4]. In recent years, a lot of research has been
done on bearing fault diagnosis based on machine learning and vibration signal, and
many useful results have been achieved [5–8]. However, the performance degradation of these methods under variable working conditions is still a challenge [9]. In
order to solve this problem, a rolling bearing fault transfer diagnosis method based
on acoustic emission signal is proposed in this paper. The goal is to build a common
space to unify the data of different distribution in the source domain and the target
domain, and realize the knowledge transfer from labeled data to unlabeled data,
which mainly solves two problems: (1) reducing the interference of signal under
J. Zhang · K. Ding (B) · H. Wang · G. Chen
China Special Equipment Inspection and Research Institute, Beijing 100029, China
e-mail: kqding@sina.com
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021
G. Shen et al. (eds.), Advances in Acoustic Emission Technology, Springer Proceedings
in Physics 259, https://doi.org/10.1007/978-981-15-9837-1_47
535
Bearing Fault Transfer Diagnosis Based
on Acoustic Emission Signal Under
Variable Working Conditions
Jiwang Zhang, Keqin Ding, Hongzhu Wang, and Guang Chen
Abstract In recent years, the intelligent diagnosis of bearing fault under stable
condition based on machine learning and vibration signal has made a lot of achievements. However, the performance degradation of these methods is obvious under
variable working conditions, which makes the intelligent diagnosis of bearing fault
still a challenge. In order to solve this problem, a rolling bearing fault transfer diagnosis method based on acoustic emission signal is proposed in this paper. The AE
data under different working conditions are collected and converted to signal map
firstly, then the sensitive features are extracted and the labeled knowledge in source
domain is transferred to the without labeled target domain by using convolutional
neural network and domain adaptation. Finally, the feasibility and superiority of the
proposed method was verified by lots of bearing experiments, and the classification
accuracy reached 88.40%, which is much higher than the traditional methods.
47.1 Introduction
Rolling bearing is the key component of rotating machinery equipment. It mainly
bears the weight and working load of the rotating body and ensures the rotating
accuracy of the rotating body [1]. Therefore, it is of great significance to monitor
and diagnose its health condition [2–4]. In recent years, a lot of research has been
done on bearing fault diagnosis based on machine learning and vibration signal, and
many useful results have been achieved [5–8]. However, the performance degradation of these methods under variable working conditions is still a challenge [9]. In
order to solve this problem, a rolling bearing fault transfer diagnosis method based
on acoustic emission signal is proposed in this paper. The goal is to build a common
space to unify the data of different distribution in the source domain and the target
domain, and realize the knowledge transfer from labeled data to unlabeled data,
which mainly solves two problems: (1) reducing the interference of signal under
J. Zhang · K. Ding (B) · H. Wang · G. Chen
China Special Equipment Inspection and Research Institute, Beijing 100029, China
e-mail: kqding@sina.com
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021
G. Shen et al. (eds.), Advances in Acoustic Emission Technology, Springer Proceedings
in Physics 259, https://doi.org/10.1007/978-981-15-9837-1_47
535