Open AccessAn Evaluation Method of DC Magnetic bias Vibration for Transformer based on Prior Knowledge and Neural Network Modeling
Author(s) -
Chenliang Zhang,
Hui Ma,
Tao Li,
Longpu Guo,
Chunlin Guo,
Hui Huang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1746/1/012008
Subject(s) - artificial neural network , transformer , vibration , computer science , fault (geology) , fuzzy logic , test set , artificial intelligence , machine learning , pattern recognition (psychology) , data mining , engineering , voltage , electrical engineering , physics , quantum mechanics , seismology , geology
At present, the number of fault samples is insufficient in the field of transformer fault diagnosis based on vibration, and the experimental data of existing research results mostly come from laboratory conditions, which is difficult to popularize in a large scale. Therefore, combining with the idea of fuzzy mathematics, this paper integrates the prior knowledge into the neural network, and extracts the function of characteristic quantity and fault probability from the trained neural network model by numerical test, which is the basis of fault diagnosis. Then, the effectiveness of this method is verified on the test sample set. The results can be used as a means to incorporate prior knowledge into intelligent algorithms, and help to study the relationship between different feature quantities. In general, this article provides a way of applying prior knowledge to vibration diagnosis algorithms, which has broad development prospects.