报告详情
A multi-source domain generalization method for bearing RUL prediction based on time-variant and time-invariant feature extraction
编号：112
访问权限：仅限参会人
更新：2024-10-23 10:00:24 浏览：160次
张贴报告
报告开始：暂无开始时间（Asia/Shanghai）
报告时间：暂无持续时间
所在会场：[暂无会议] [暂无会议段]
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摘要
Deep learning-based remaining useful life (RUL)
prediction methods have demonstrated significant advantages
due to their powerful feature representation capabilities. How
ever, existing learning models struggle to effectively distinguish
between the time-varying and time-invariant characteristics of
vibration signals, which hinders their generalization perfor
mance. To address this issue, we propose a domain generalization
method based on the extraction of time-varying and time
invariant features, tailored to the characteristics of bearing
vibration time series signals. Firstly, Fourier transform and
inverse Fourier transform are employed to isolate high-frequency
features, obtaining both time-varying and time-invariant signals.
The time-varying signals are then processed using an attention
mechanism to extract time-varying features and learn the bearing
degradation trend. Concurrently, the time-invariant signals are
fed into an encoder-decoder structure to extract their invariant
features. Finally, both sets of features are input into a Gate
Recurrent Unit (GRU) module for RUL prediction. Experimental
results across three tasks demonstrate that our model achieves
excellent generalization capabilities.
关键词
bearing,RUL prediction,time variant-time invariant,multi-source domain generalization,fast fourier transform
报告人
正宇邓
Mr Hefei University of Technology 娟徐
Ms Hefei University of Technology稿件作者
全部评论
重要日期
-
会议日期
10月31日
2024
至11月03日
2024
-
09月30日 2024
初稿截稿日期
-
11月12日 2024
注册截止日期
主办单位
Anhui University
Xi’an Jiaotong University
Harbin Institute of Technology
IEEE Instrumentation & Measurement Society
Xi’an Jiaotong University
Harbin Institute of Technology
IEEE Instrumentation & Measurement Society
联系方式
- Mrs. icsmd
- ic******@163.com
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