Scholarlink

<p>Remaining useful life (RUL) is a significant challenge in prognostics and health management. Existing methods suffer from a severe performance drop, as testing data from engine sensors exhibits high nonlinearity and complicated fault modes. In this paper, the authors introduce a reinforcement neural architecture search technique based on upper confidence bound (UCB) to optimize an efficient model. UCB explores the combinatorial parameter space of a multi-head convolutional layers concatenate with recurrent layers to search for a suitable architecture. To address the highly nonlinear dataset in complicated working conditions, rainflow counting algorithm is applied to extract features. Experiments are conducted on C-MAPSS dataset. Compared with state-of-the-art, the proposed approach yields better results in both RMSE and scoring function for all the sub-datasets. In multiple working conditions, the authors achieve lower RMSE with significant superiority. The experimental results confirm that the proposed method is an efficient approach for obtaining highly precise RUL predictions.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Efficient Architecture Search for Remaining Useful Life Prediction Using Rainflow Counting Features

  • 期刊论文

  • 发表日期：

    2025年7月3日

查看全文
  • Pengli Mao,
  • Yan Lin,
  • Lin Li,
  • Baochang Zhang

摘要

Remaining useful life (RUL) is a significant challenge in prognostics and health management. Existing methods suffer from a severe performance drop, as testing data from engine sensors exhibits high nonlinearity and complicated fault modes. In this paper, the authors introduce a reinforcement neural architecture search technique based on upper confidence bound (UCB) to optimize an efficient model. UCB explores the combinatorial parameter space of a multi-head convolutional layers concatenate with recurrent layers to search for a suitable architecture. To address the highly nonlinear dataset in complicated working conditions, rainflow counting algorithm is applied to extract features. Experiments are conducted on C-MAPSS dataset. Compared with state-of-the-art, the proposed approach yields better results in both RMSE and scoring function for all the sub-datasets. In multiple working conditions, the authors achieve lower RMSE with significant superiority. The experimental results confirm that the proposed method is an efficient approach for obtaining highly precise RUL predictions.