The extraction of rolling bearing fault features using traditional diagnostic methods is not sufficiently comprehensive and the features are often chosen subjectively and depend on human experience. In this paper, an improved deep convolutional process is used to extract a set of features adaptively. The hidden multi-layer feature of deep convolutional neural networks is also exploited to improve the extraction features. A deterministic detection of low-confidence samples is performed to ensure the reliability of the recognition results and to decrease the rate of false positives by evaluating the diagnosis of the deep convolutional neural network. To improve the efficiency of the continuous learning elements of the rolling bearing fault diagnosis, a clone learning strategy based on cloning and mutation operations is proposed. The experimental results show that the proposed deep convolutional neural network model can extract multiple rolling bearing fault features, improve classification and detection accuracy by reducing the false positive rate when diagnosing rolling bearing faults, and accelerate learning efficiency when using low-confidence rolling bearing fault samples.
类型: 期刊论文
作者: Yuling Tian,Xiangyu Liu
来源: Tsinghua Science and Technology 2019年06期
年度: 2019
分类: 工程科技Ⅱ辑,信息科技
专业: 机械工业,自动化技术
单位: the College of Information and Computer, Taiyuan University of Technology
基金: supported by the National Natural Science Foundation of China (No. 61472271)
分类号: TH133.33;TP18
页码: 750-762
总页数: 13
文件大小: 676K
下载量: 80
本文来源: https://www.lunwen90.cn/article/f19d4c884889f3610fdab99a.html