Review of Relay Life Prediction Methods Based on Deep Learning

doi:10.16628/j.cnki.2095-8188.2021.12.001

Abstract

Abstract:

In recent years,with the continuous development of science and technology,the remaining service life of relay and other electrical components has gradually become a research hotspot.The accurate and efficient analysis of massive monitoring data is the main task of fault detection and health management.Because the traditional methods are limited to rely on the physical models and prior knowledge,the deep learning methods emerge as the times require.First,the current research status of the remaining life of relays at home and abroad is introduced.Second,it analyzes the remaining life prediction method based on deep learning in detail is analyszed,and the advantages and disadvantages of each method are summarized.Finally,the future is looked forward to.

Key words: deep learning, life prediction, neural network, PHM

CLC Number:

TM58

LIU Baixin, WANG Zhaobin, QIAO Qingyun, ZHU Jiamiao, LI Zhen. Review of Relay Life Prediction Methods Based on Deep Learning[J]. LOW VOLTAGE APPARATUS, 2021, 0(12): 1-6.

Figures/Tables 4

References 45

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方法	优点	缺点
DNN	模型简单	易产生数据过拟合,计算量大
CNN	可以处理高维数据	数据降维时易造成信息丢失
DBN	反向微调	难以预测长时间序列
RNN	可以利用历史信息	易产生梯度消失