Fault diagnosis of mind-used transformer based on stacked sparse auto-encoder
| Autor: | XU Qianwen, JI Xingquan, ZHANG Yuzhen, LI Jun, YU Yongjin |
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| Jazyk: | čínština |
| Rok vydání: | 2018 |
| Předmět: | |
| Zdroj: | Gong-kuang zidonghua, Vol 44, Iss 10, Pp 33-37 (2018) |
| Druh dokumentu: | article |
| ISSN: | 1671-251X 1671-251x |
| DOI: | 10.13272/j.issn.1671-251x.2018040092 |
| Popis: | In view of application of deep learning to transformer fault diagnosis had a good fault diagnosis effect, a fault diagnosis method of mind-used transformer based on stacked sparse auto-encoder was proposed. Sparse auto-encoder is constructed by introducing sparse item constraint in hidden layer of auto-encoder, then the multiple sparse auto-encoders are stacked to form stacked sparse auto-encoder, and Softmax classifier is used as output layer to establish mine-used transformer fault diagnosis model. A large number of unlabeled samples are used to carry out unsupervised pre-training for the model, and the model parameters are optimized through supervised fine-tuning. The example analysis results show that stacked sparse auto-encoder is more accurate than stack auto-encoder in application of fault diagnosis of mind-used transformer. |
| Databáze: | Directory of Open Access Journals |
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