Liang Ma
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Liang Ma has a total of 12 co-authors. Here are the top co-authors by number of shared publications:
- Chen Lu
Hangzhou International Innovation Institute, Beihang University, Hangzhou, China
2 shared publications - Laifa Tao
Hangzhou International Innovation Institute Beihang University Hangzhou China
2 shared publications - Yu Ding
Hangzhou International Innovation Institute, Beihang University, Hangzhou 311115, China; School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China; Science & Technology on Reliability & Environmental Engineering Laboratory, Beijing 100191, China; Institute of Reliability Engineering, Beihang University, Beijing 100191, China
2 shared publications Chao Wang
School of Reliability and Systems Engineering, Beihang University, Beijing, China
1 shared publication- Di Peng
Huazhong University of Science and Technology
1 shared publication Jia Liu
The Fifth Electronic Research Institute of Ministry of Industry and Information Technology
1 shared publication
Latest Publications
- 31 January 2023
- Published by Springer Nature
- p. 2647-2657
Fault Diagnosis of Hydraulic Systems with Missing Data Based on FGCN: A Data Augmentation Approach
- 31 January 2023
- Published by Springer Nature
- p. 2647-2657
Fault Diagnosis of Hydraulic Systems with Missing Data Based on FGCN: A Data Augmentation Approach
- Open Access
- 14 October 2019
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 7, 149736-149749
A Generative Adversarial Network-Based Intelligent Fault Diagnosis Method for Rotating Machinery Under Small Sample Size Conditions
Rotating machinery plays a key role in mechanical equipment, and the fault diagnosis of rotating machinery is a popular research topic. To overcome the dependency on expert knowledge regarding conventional time-frequency analysis diagnosis methods, machine learning (ML) and artificial intelligence (AI)-based methods are commonly studied. Although these methods can achieve high-accuracy diagnosis results, they are based on a large number of training samples. A generative adversarial network (GAN) is an algorithm with the capability of generating realistic samples that are similar to the real samples, and it can be applied to solve fault diagnosis problems with insufficient training data, which is called the small sample size condition in this study. However, a single-GAN model cannot achieve a good diagnostic result. To achieve adaptive feature extraction and high diagnosis accuracy, this study proposes an intelligent fault diagnosis method for rotating machinery based on GANs under small sample size conditions. The effectiveness and performance of the proposed method are validated using rolling bearing and gearbox datasets. In these datasets, only 10% and 20% of the samples are selected as the training data. Samples associated with different health conditions and various working conditions are included in the datasets. Compared with those of other diagnosis methods, the high-accuracy and low-volatility diagnosis results indicate that the proposed method can stably distinguish fault modes under different working conditions in an adaptive way, even though few training samples are available.
- 14 October 2019
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 7, 149736-149749
Open AccessA Generative Adversarial Network-Based Intelligent Fault Diagnosis Method for Rotating Machinery Under Small Sample Size Conditions
Rotating machinery plays a key role in mechanical equipment, and the fault diagnosis of rotating machinery is a popular research topic. To overcome the dependency on expert knowledge regarding conventional time-frequency analysis diagnosis methods, machine learning (ML) and artificial intelligence (AI)-based methods are commonly studied. Although these methods can achieve high-accuracy diagnosis results, they are based on a large number of training samples. A generative adversarial network (GAN) is an algorithm with the capability of generating realistic samples that are similar to the real samples, and it can be applied to solve fault diagnosis problems with insufficient training data, which is called the small sample size condition in this study. However, a single-GAN model cannot achieve a good diagnostic result. To achieve adaptive feature extraction and high diagnosis accuracy, this study proposes an intelligent fault diagnosis method for rotating machinery based on GANs under small sample size conditions. The effectiveness and performance of the proposed method are validated using rolling bearing and gearbox datasets. In these datasets, only 10% and 20% of the samples are selected as the training data. Samples associated with different health conditions and various working conditions are included in the datasets. Compared with those of other diagnosis methods, the high-accuracy and low-volatility diagnosis results indicate that the proposed method can stably distinguish fault modes under different working conditions in an adaptive way, even though few training samples are available.