Optimising the performance of SiC-based varistors through composition and microstructure control

Fernández-García, Lucía; Azough, Feridoon; Parsons, Gareth; Quadling, Amanda; Wang, Bing; Freer, Robert

doi:10.1016/j.jeurceramsoc.2021.10.034

Cited by 5 publications

(1 citation statement)

References 21 publications

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“…Nahm et al [13] found that the nonlinear coefficient has a great relationship with the morphology of the grain boundary. At the same time, there are many researchers and scholars committed to improving the nonlinear coefficient of varistors [14][15][16][17][18], but there are few studies that have applied the nonlinear coefficient as the main parameter to construct the lifetime model.…”

Section: Introductionmentioning

confidence: 99%

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

Ruan,

Cheng,

Bai

et al. 2023

Applied Sciences

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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.

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