Chrome Extension
WeChat Mini Program
Use on ChatGLM

SSA优化深度双向门控循环单元网络的轴承性能退化趋势预测

CHEN Renxiang, CHEN Guorui, XU Xiangyang, HU Xiaolin, ZHANG Yanfeng

Journal of Vibration and Shock(2023)

Cited 0|Views0
No score
Abstract
为在非经验指导下获取双向门控循环单元网络中最优隐藏层单元数,实现滚动轴承性能退化趋势预测,提出基于麻雀搜索算法优化深度双向门控循环单元的轴承性能退化趋势预测方法.首先,在正向门控循环单元网络基础上,增加反向门控循环单元网络,以构建深度双向门控循环单元预测网络;然后,将预测值与真实值的均方误差作为适应度值,根据麻雀发现者和捕食者进行参数更新,经优化后获得最优隐藏层单元参数下的深度双向门控循环单元网络预测模型;最后,通过全连接层实现性能退化趋势预测.在公共数据集与实测数据集上进行试验验证,验证了所提方法的有效性与可行性.
More
Key words
rolling bearing,performance degradation trend prediction,sparrow search algorithm,optimization parameters,fitness
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined