| NOVEL SEMI-SUPERVISED FAULT DIAGNOSIS METHOD COMBINING
TRI-TRAINING AND DEEP BELIEF NETWORK FOR CHARGING
EQUIPMENT OF ELECTRIC VEHICLE |
| Dexin Gao , Xihao Lin , Xiaoyu Zheng , Qing Yang |
1College of Automation and Electronic Engineering, Qingdao University of Science & Technology
2College of Information Science and Technology, Qingdao University of Science & Technology |
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| ABSTRACT |
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It is of great significance to make fault diagnosis for charging equipment of electric vehicle (EV) accurately
and timely. The methods based on deep learning are promising because they can extract fault features and conduct fault
diagnosis effectively. However, deep learning networks require a large number of labeled samples for model training, and in
practical conditions, the labeled samples available of charging equipment are quite limited. To address these problems, we,
inspired by semi-supervised learning, proposed a semi-supervised charging equipment fault diagnosis method combining
Tri-training and deep belief network (DBN). The proposed method adopts Tri-training to enable full utilization of unlabeled
data of charging equipment, and obtain a large amount of valid pseudo-labeled data, which will be used for the training of
Tri-DBN model. Subsequently, the fault features of charging equipment are input into Tri-DBN, which is used for the
classification and identification of charging equipment faults. The experimental results show that the proposed method
effectively improves the faults classification accuracy of charging equipment and show more than 90 % accuracy at all fault
types. In addition, this method still performs well in the presence of less labeled data. |
| Key Words:
EV, Charging equipment, Semi-supervised learning, Tri-training, DBN, Fault diagnosis |
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Corresponding Author.
Qing Yang , Email. 
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