用户名 密 码 登 录 忘记密码 免费注册

网站会员中心 | 设为首页 | 加入收藏

 您好，欢迎光临中国轴承行业网         退出登陆

会员中心 | 设为首页 | 加入收藏

协会

统计

国外文献 > 1.轴承 > 5. 监测与诊断 > 寿命与维护

关键词搜索：

标 题： A Remaining Useful Life Prediction Technique for Rolling Element Bearings Based on Deep Temporal Feature Transfer
基于深度时序特征迁移的滚动轴承剩余使用寿命预测技术
作 者： Yajun Shang
David Mba
Xiaochuan Li
Tian Ran Lin
关键词： Rolling element bearing  [滚动轴承]
Remaining useful life  [ 剩余使用寿命]
Ordered neuron long-short term memory network  [ 有序神经元长短期记忆网络]
Transfer learning  [ 迁移学习]
出版年： 2024
文集名： Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023), Volume 1
联合国大会、DAMAS、IncoME和TEPEN会议联合会议（UNIfied 2023）会议纪要，第一卷
会议名： 2023 International Conference on Damage Assessment of Structures (DAMAS 2023), 2023 International Conference on Maintenance Engineering (IncoME 2023), 2023 Conference of the Efficiency and Performance Engineering Network (TEPEN 2023)
2023年结构损伤评估国际会议（DAMAS 2023）、2023年维护工程国际会议（IncoME 2023）、2023年效率与性能工程网络会议（TEPEN 2023）
届 次：
摘 要： This paper presents a bearing remaining useful life (RUL) prediction technique based on an ordered neuron long-short term memory network (ON-LSTM) and a transfer learning. The combined technique can largely improve the RUL prediction accuracy by considering the distribution difference and temporal dependence between the bearing degradation data. In this approach, the ordered feature information within the data is extracted using the ON-LSTM to form the health indicators of the source domain. The distance and distribution of both source and target domains from the bearing degradation data are then adaptively reduced using the transfer learning algorithm. Finally, the degradation trend learned from the source domain can be mapped and transferred to the unlabeled data of the target domain for the bearing remaining useful life prediction. The robustness of the proposed technique is evaluated using the published 2012 IEEE-PHM Challenge bearing lifecycle data. The result shows that the proposed technique can accurately capture the degradation trend of bearings using unlabeled target-domain data.

本文提出一种基于有序神经网络长短期记忆网络（ON-LSTM）与迁移学习的轴承剩余使用寿命（RUL）预测方法。该联合技术通过考虑轴承劣化数据之间的分布差异和时间依赖性，显著提升了RUL预测精度。在本方法中，利用ON-LSTM提取数据中的有序特征信息，构建源域的健康指标。随后，采用迁移学习算法自适应地降低源域与目标域相对于轴承劣化数据的距离和分布。最后，从源域学习到的劣化趋势可被映射并迁移至目标域未标注的数据，用于轴承剩余使用寿命的预测。该方法的鲁棒性通过2012年IEEE-PHM挑战赛公布的轴承生命周期数据进行了评估。结果表明，所提方法能够准确捕捉未标注的目标域数据中的轴承劣化趋势。
中文内容为网络AI翻译，专业术语未必准确，仅供参考。

VIP网员

联系我们 | 登录注册 | 会员管理 | 网员服务 | 服务信息 | 广告服务 | 高薪聘请 | 法律声明 | 关于我们

京公网安备 11010202009788    京ICP备2021015424号