基于LSTM的汽车衡传感器寿命预测分析
Analysis of Life Prediction of Automotive Scale Sensors Based on LSTM
游晓锋 1黄胜1
作者信息
摘要
阐述一种基于LSTM的汽车衡传感器寿命预测方法,通过历史数据的学习与分析,预测传感器未来状态.实验结果显示,该方法在预测精度上优于传统统计方法和其他机器学习算法,为传感器的维护和更换提供了更准确的依据.
Abstract
This paper describes an LSTM-based life prediction method for vehicle weighing sensors to predict the future state of the sensors by learning and analyzing the historical data.The experimental results show that the proposed method outperforms traditional statistical methods and other machine learning algorithms in terms of prediction accuracy,providing a more accurate basis for sensor maintenance and replacement.
关键词
寿命预测/长短期记忆网络(LSTM)/时间序列Key words
lifetime prediction/long short-term memory network(LSTM)/time series引用本文复制引用
出版年
2024
NETL
NSTL
万方数据