学习迁移
计算机科学
人工智能
方位(导航)
卷积神经网络
计算
正规化(语言学)
辍学(神经网络)
深度学习
断层(地质)
人工神经网络
模式识别(心理学)
机器学习
算法
数据挖掘
地质学
地震学
作者
Zhi Zheng,Jiuman Fu,Chuanqi Lu,Yong Zhu
出处
期刊:Measurement
[Elsevier]
日期:2021-06-01
卷期号:177: 109285-109285
被引量:32
标识
DOI:10.1016/j.measurement.2021.109285
摘要
Due to limited conditions of production sites, only the small fault dataset (target dataset) of the rolling bearing can be collected, which leads to the failure construction of the effective deep learning network. Aiming at the above problems, the sufficient fault dataset (source dataset) of other type of rolling bearing is introduced as the auxiliary, and thus a new transfer learning network based on convolutional neural network (CNN) is proposed. The new transfer learning network is with a new structure, and it is trained by a new training strategy, and then it is optimized by a new optimal fusion method of dropout layer 4 and L2 regularization. The measured fault signals of the rolling bearings are tested and verified, and results demonstrate that the proposed transfer learning network has low computation cost, high accuracy and strong diagnosis ability. Furthermore, it performs much better than the traditional transfer learning networks.
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