自编码
特征(语言学)
计算机科学
降级(电信)
过程(计算)
学习迁移
领域(数学分析)
传输(计算)
模式识别(心理学)
特征提取
堆栈(抽象数据类型)
时域
人工智能
工程类
深度学习
数学
计算机视觉
电信
数学分析
哲学
语言学
并行计算
操作系统
程序设计语言
作者
Yisheng Zou,Zhixuan Li,Yongzhi Liu,Shijiao Zhao,Yantao Liu,Guofu Ding
出处
期刊:Measurement
[Elsevier]
日期:2022-01-01
卷期号:188: 110393-110393
被引量:21
标识
DOI:10.1016/j.measurement.2021.110393
摘要
Predicting the remaining useful life (RUL) of rolling bearings under different working conditions improved significantly by transfer learning. However, existing methods have not studied the following problems thoroughly: (1) The influence of the discrepancy between features of different dimensions on the feature transfer process; (2) The feature transfer process in the degradation stage with apparent discrepancy has a significant influence on the transfer prediction of remaining useful life. In this study, a degradation occurrence time identification method based on the distribution differences in reconstructing degradation indicators has been proposed to obtain samples of degradation stages. A stack convolutional autoencoder model based on a multi-domain adversarial network is also proposed to reduce the impact of discrepancies among extracted degradation features on the feature transfer process. As per the experimental results, it was found that the proposed method can effectively improve the RUL prediction accuracy.
科研通智能强力驱动
Strongly Powered by AbleSci AI