Predicting the Life of Varistors via a Nonlinear Coefficient Based on a Small-Scale Data Model

With the great leaps forward in the development of the railway, the importance of safe operation and maintenance has become increasingly prominent. Varistor is the key component insurge protective devices for railway communication and signaling equipment, it is necessary to study a description model...

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Bibliographic Details
Published in:Applied sciences 2024-01, Vol.14 (1), p.171
Main Authors: Ruan, Xiaofei, Cheng, Weidong, Bai, Yihao, Wen, Weigang
Format: Article
Language:English
Subjects:
BiLSTM
Breakdowns
Grain boundaries
life prediction
Neural networks
nonlinear coefficient
Performance evaluation
Trends
varistor
Zinc oxides
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Summary:With the great leaps forward in the development of the railway, the importance of safe operation and maintenance has become increasingly prominent. Varistor is the key component insurge protective devices for railway communication and signaling equipment, it is necessary to study a description model of the varistor degradation process and predict its lifetime through condition monitoring. Among the monitoring parameters, nonlinear coefficient is an important index to measure the health of varistors. Considering that the degradation process of varistors is the cumulative effect of surge history, and its nonlinear coefficient has a time–series relationship, a life prediction model for varistors based on BiLSTM is proposed. The model innovatively uses nonlinear coefficient as the characterization of the deterioration degree of varistors and uses a small-scale network to predict the development trend of nonlinear coefficient automatically and accurately. Verified by surge impact experiments, the model can accurately predict the state of nonlinear coefficient according to historical data and has the potential for engineering applications in predicting the life of varistors.
ISSN:2076-3417
2076-3417
DOI:10.3390/app14010171