- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | YANG Yueyi, WANG Lide, WANG Chong, WANG Huizhen, LI Ye. Fault Diagnosis Method Based on Deep Active Learning For MVB Network[J]. Journal of Southwest Jiaotong University, 2022, 57(6): 1342-1348, 1385. doi: 10.3969/j.issn.0258-2724.20210195
|
Multiple vehicle bus (MVB) is employed to transmit important train operation control instructions and monitoring information, and accurate diagnosis of the fault types of MVB network is the basis of the intelligent operation and maintenance system. To this end, a fault diagnosis method for MVB network is proposed, which combines the active learning and deep neural networks. It adopts the stacked denoising autoencoder to automatically extract physical features from the electrical MVB signals; then the features are used to train a deep neural network classifier for identifying MVB fault classes. An efficient active learning method based on uncertainty and credibility can solve the problems of insufficient labeled samples and high costs of manual labeling in practical application. It can build a competitive classifier with a small number of labeled training samples. Experiment results demonstrate that to achieve a high accuracy above 90%, the proposed method requires 600 labeled training samples, which is less than 2800 labeled training samples required by random sampling method. With the same number of labeled samples, the proposed method can achieve the better performance as to three different metrics than traditional methods.
| [1] |
LUEDICKE D, LEHNER A. Train communication networks and prospects[J]. IEEE Communications Magazine, 2019, 57(9): 39-43. doi: 10.1109/MCOM.001.1800957
|
| [2] |
李召召,王立德,岳川,等. 基于MKLSVM的MVB端接故障诊断[J]. 北京交通大学学报,2019,43(2): 100-106. doi: 10.11860/j.issn.1673-0291.20180128
LI Zhaozhao, WANG Lide, YUE Chuan, et al. Terminating fault diagnosis of MVB based on MKLSVM[J]. Journal of Beijing Jiaotong University, 2019, 43(2): 100-106. doi: 10.11860/j.issn.1673-0291.20180128
|
| [3] |
LI Z Z, WANG L D, YANG Y Y. Fault diagnosis of the train communication network based on weighted support vector machine[J]. IEEJ Transactions on Electrical and Electronic Engineering, 2020, 15(7): 1077-1088. doi: 10.1002/tee.23153
|
| [4] |
KIRANYAZ S, INCE T, ABDELJABER O, et al. 1-D convolutional neural networks for signal processing applications[C]//IEEE International Conference on Acoustics, Speech and Signal Processing. New York: IEEE, 2019: 8360-8364.
|
| [5] |
WANG Y L, PAN Z F, YUAN X F, et al. A novel deep learning based fault diagnosis approach for chemical process with extended deep belief network[J]. ISA Transactions, 2020, 96: 457-467. doi: 10.1016/j.isatra.2019.07.001
|
| [6] |
LU C, WANG Z Y, QIN W L, et al. Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification[J]. Signal Process, 2017, 130: 377-388. doi: 10.1016/j.sigpro.2016.07.028
|
| [7] |
DE BRUIN T, VERBERT K, BABUSKA R. Railway track circuit fault diagnosis using recurrent neural networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(3): 523-533. doi: 10.1109/TNNLS.2016.2551940
|
| [8] |
CAO X Y, YAO J, XU Z B, et al. Hyperspectral image classification with convolutional neural network and active learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(7): 4604-4616. doi: 10.1109/TGRS.2020.2964627
|
| [9] |
BI H X, XU F, WEI Z Q, et al. An active deep learning approach for minimally supervised PolSAR image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(11): 9378-9395. doi: 10.1109/TGRS.2019.2926434
|
| [10] |
ZHANG A M, LI B H, WANG W H, et al. MII:a novel text classification model combining deep active learning with BERT[J]. CMC-Comput. Mat. Contin, 2020, 63(3): 1499-1514.
|
| [11] |
ZHAO Xiukuan, LI Min, XU Jinwu, et al. An effective procedure exploiting unlabeled data to build monitoring system[J]. Expert Systems with Applications, 2011, 38(8): 10199-10204. doi: 10.1016/j.eswa.2011.02.078
|
| [12] |
PENG Peng, ZHANG Wenjia, ZHANG Yi, et al. Cost sensitive active learning using bidirectional gated recurrent neural networks for imbalanced fault diagnosis[J]. Neurocomputing, 2020, 407: 232-245. doi: 10.1016/j.neucom.2020.04.075
|
| [13] |
KUMAR P, GUPTA A. Active learning query strategies for classification, regression, and clustering: a survey[J]. Journal of Computer Science and Technology, 2020, 35(4): 913-945. doi: 10.1007/s11390-020-9487-4
|
| [14] |
RAHHAL M M Al, BAZI Y, ALHICHRI H, et al. Deep learning approach for active classification of electrocardiogram signals[J]. Information Science, 2016, 345: 340-354. doi: 10.1016/j.ins.2016.01.082
|
| [15] |
JIANG P, HU Z X, LIU J, et al. Fault diagnosis based on chemical sensor data with an active deep neural network[J]. Sensors, 2016, 16(10): 1695
|
| [16] |
SHIM J, KANG S, CHO S. Active learning of convolutional neural network for cost-effective wafer map pattern classification[J]. IEEE Transactions on Semiconductor Manufacturing, 2020, 33(2): 258-266. doi: 10.1109/TSM.2020.2974867
|
| [17] |
朱琴跃,谢维达,谭喜堂. MVB协议一致性测试研究与实现[J]. 铁道学报,2007,29(4): 115-120. doi: 10.3321/j.issn:1001-8360.2007.04.024
ZHU Qinyue, XIE Weida, TAN Xitang. Research on MVB protocol conformance testing[J]. Journal of the China Railway Society, 2007, 29(4): 115-120. doi: 10.3321/j.issn:1001-8360.2007.04.024
|
| [18] |
CHEN M, ZHU K, WANG R, et al Dusit. active learning-based fault diagnosis in self-organizing cellular networks[J]. IEEE Communications Letters, 2020, 24(8): 1734-1737. doi: 10.1109/LCOMM.2020.2991449
|
| [1] | JIANG Qilong, YAO Weifeng, ZHANG Ye. Fault Diagnosis of Suspended Electromagnet Based on Current Change Rate Increment[J]. Journal of Southwest Jiaotong University, 2025, 60(4): 1042-1049. doi: 10.3969/j.issn.0258-2724.20250067 |
| [2] | CHENG Yao, CHEN Bingyan, ZHANG Weihua, LI Fuzhong. Fault Diagnosis of Axle-Box Bearing Based on Weighted Combined Improved Envelope Spectrum[J]. Journal of Southwest Jiaotong University, 2024, 59(1): 142-150. doi: 10.3969/j.issn.0258-2724.20220019 |
| [3] | HAN Jialin, GAO Hongli, GUO Liang, LIU Xue, WU Tingting, LI Shichao. Bearing Fault Diagnosis Method Based on Order Tracking Without Rotational Speed[J]. Journal of Southwest Jiaotong University, 2024, 59(1): 220-228. doi: 10.3969/j.issn.0258-2724.20220116 |
| [4] | CHEN Bingyan, GU Fengshou, ZHANG Weihua, SONG Dongli, CHENG Yao. Axle-Box Bearing Fault Diagnosis Based on Multiband Weighted Envelope Spectrum[J]. Journal of Southwest Jiaotong University, 2024, 59(1): 201-210. doi: 10.3969/j.issn.0258-2724.20220047 |
| [5] | DU Jiahao, QIN Na, JIA Xinming, ZHANG Yiming, HUANG Deqing. Fault Diagnosis of Multiple Railway High Speed Train Bogies Based on Federated Learning[J]. Journal of Southwest Jiaotong University, 2024, 59(1): 185-192. doi: 10.3969/j.issn.0258-2724.20220120 |
| [6] | CUI Hongtao, CAO Ke, ZHANG Hu, CUI Xiao. Weather Classification in Traffic Scene Based on Joint Voting Network[J]. Journal of Southwest Jiaotong University, 2021, 56(3): 579-586. doi: 10.3969/j.issn.0258-2724.20200084 |
| [7] | WANG Zicheng, ZHANG Yadong, GUO Jin, SU Lina, YANG Jing, SONG Ci, LI Kehong. Fault Diagnosis for Track Circuit Based on Interval Type-2 Neural-Fuzzy System[J]. Journal of Southwest Jiaotong University, 2021, 56(1): 190-196. doi: 10.3969/j.issn.0258-2724.20180983 |
| [8] | HOU Jin, LÜ Zhiliang, XU Mao, WU Peijun, LIU Yuling, ZHANG Xiaoyu, CHENG Zeng. Combined Neural Networks Based on Deep Learning for Signal Detection in Aeronautical Communications[J]. Journal of Southwest Jiaotong University, 2019, 54(4): 863-869, 878. doi: 10.3969/j.issn.0258-2724.20180164 |
| [9] | ZHOU Qizhong, XIE Yongle. Fault Diagnosis Method for Analog Circuits Based on Matrix Perturbation Analysis[J]. Journal of Southwest Jiaotong University, 2017, 30(2): 369-378. doi: 10.3969/j.issn.0258-2724.2017.02.022 |
| [10] | YE Qing, LIU Changhua, PAN Hao. Simultaneous Fault Diagnosis Method Based on Improved Sparse Bayesian Extreme Learning Machine[J]. Journal of Southwest Jiaotong University, 2016, 29(4): 792-799. doi: 10.3969/j.issn.0258-2724.2016.04.026 |
| [11] | ZHAO Lin-Hai, MU Jian-Cheng. Fault Diagnosis Method for Jointless Track Circuit Based on AOK-TFR[J]. Journal of Southwest Jiaotong University, 2011, 24(1): 84-91. doi: 10.3969/j.issn.0258-2724.2011.01.013 |
| [12] | XIAO Luan, WANG Tai-yong, QINXu-da, WAN Shu-min, LIU Song-tang. Remote Virtual Fault Diagnosis System of Equipment Based on COM[J]. Journal of Southwest Jiaotong University, 2003, 16(5): 566-569. |
| [13] | QIAN Shu-hua, WANG Xin-qing, LIHuan-liang. Application of Quadric Time-Frequency Distribution to Mechanical Fault Diagnosis[J]. Journal of Southwest Jiaotong University, 2003, 16(5): 578-580. |
| [14] | QINPing, YANBing, TANDa-ming. Study on Fault Diagnosis of Sliding Bearings Using AE Signals[J]. Journal of Southwest Jiaotong University, 2001, 14(3): 272-275. |
| [15] | WANGJue, LI Zhi. A Fault Diagnosis of Dynamic Systems Based on Unknown Input Observer Schemes[J]. Journal of Southwest Jiaotong University, 2001, 14(3): 281-285. |
| [16] | DAIXiao-wen, ZHONG Gui-ying, WUHao-zhong. Application of the Wavelet Transform to the Failure Diagnosis of Tilting Trains[J]. Journal of Southwest Jiaotong University, 2000, 13(6): 651-655. |
| 1. | 詹宝容，庾锡昌. 联合收割机裂纹转子与滚动轴承故障诊断系统研究——基于卷积神经网络. 农机化研究. 2024(05): 187-191 .  |
Figures(7) / Tables(2)
YANG Yueyi, WANG Lide, WANG Chong, WANG Huizhen, LI Ye. Fault Diagnosis Method Based on Deep Active Learning For MVB Network[J]. Journal of Southwest Jiaotong University, 2022, 57(6): 1342-1348, 1385. doi: 10.3969/j.issn.0258-2724.20210195
DownLoad:
