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Deep Learning -Based Remaining Life Prediction for DC-Link Capacitor in High Speed Train
编号：128 访问权限：仅限参会人 更新：2023-11-20 13:53:19 浏览：255次 张贴报告

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摘要
The accurate prediction of the remaining useful life of DC-link capacitors is crucial in high-speed railway traction drive operating conditions. This prediction takes into account environmental factors, physical structure, and strict safety operation requirements. In this paper, we propose a method for predicting remaining life using deep learning optimized by the particle swarm algorithm. Firstly, the upper and lower voltage of the capacitor are selected as the characteristic values. By denoising the voltage with wavelet transform, we calculate failure thresholds for both ends of the support capacitor. Then, we amplify the input weights in real-time using the Long Short-Term Memory (LSTM) neural network with a macro-micro attention mechanism. Finally, we utilize the particle swarm optimization algorithm to optimize the number of input units and the learning rate of the neural network for the purpose of predicting lifetime. The effectiveness of this method is verified through model evaluation indices in a case study of high speed train traction system.
 
关键词
Residual life prediction, support capacitors, long short-term memory neural networks, macro-micro attention mechanism, particle swarm optimization.
报告人
Zhang Kunpeng
Lecturer East China Jiaotong University

稿件作者
Zhang Kunpeng East China Jiaotong University
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重要日期

  • 会议日期

    12月08日

    2023

    至

    12月10日

    2023

  • 11月01日 2023

    初稿截稿日期

  • 12月10日 2023

    注册截止日期

主办单位
IEEE IAS
承办单位
Southwest Jiaotong University (SWJTU)
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