Intelligent fault diagnosis of rolling bearings using a semi-supervised convolutional neural network
Wu, Yaochun1,2; Zhao, Rongzhen1; Jin, Wuyin1; He, Tianjing1; Ma, Sencai1; Shi, Mingkuan1
2021-04
发表期刊Applied Intelligence
ISSN0924-669X
卷号51期号:4页码:2144-2160
摘要

The success of convolutional neural networks (CNNs) in intelligent fault diagnosis is largely dependent on massive amounts of labelled data. In a real-world case, however, massive amounts of labelled data are difficult or costly to collect, whereas abundant unlabelled data are often available. To utilize such unlabelled data, a novel method using a semi-supervised convolutional neural network (SSCNN) for intelligent fault diagnosis of bearings is proposed. First, a 1-d CNN is applied to learn class space features and generate class probabilities of unlabelled samples, based on which a class probability maximum margin criterion (CPMMC) method is used to construct the loss function of unlabelled samples. Then, the constructed loss function, which aims to maximise the inter-class distance of class space features and minimise the intra-class distance of class space features, is integrated into the cross-entropy loss function of the CNN, and the SSCNN is established. Finally, the SSCNN model is applied to analyse the vibration signals collected from rolling bearings, and a novel intelligent fault diagnosis method using the SSCNN is proposed. Two datasets are employed to validate the effectiveness of the proposed methodology. The results show that the established SSCNN can effectively utilise unlabelled samples to train the model and enhance its fault diagnosis performance. Through a comparison with commonly used semi-supervised deep learning methods, the superiority of the proposed method is validated. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.

关键词Convolution Deep learning Failure analysis Fault detection Learning systems Roller bearings Semi-supervised learning Vibration analysis Class probabilities Diagnosis performance Intelligent fault diagnosis Inter-class distance Learning methods Maximum margin criterions Rolling bearings Vibration signal
DOI10.1007/s10489-020-02006-6
收录类别SCI ; SCIE ; EI
语种英语
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000582808200002
出版者Springer
EI入藏号20204409426163
EI主题词Convolutional neural networks
EI分类号601.2 Machine Components ; 716.1 Information Theory and Signal Processing
来源库WOS
引用统计
被引频次:18[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符https://ir.lut.edu.cn/handle/2XXMBERH/148404
专题机电工程学院
通讯作者Zhao, Rongzhen
作者单位1.Lanzhou Univ Technol, Sch Mech & Elect Engn, Lanzhou 730050, Peoples R China;
2.Anyang Inst Technol, Sch Mech Engn, Anyang 455000, Peoples R China
第一作者单位兰州理工大学
通讯作者单位兰州理工大学
第一作者的第一单位兰州理工大学
推荐引用方式
GB/T 7714
Wu, Yaochun,Zhao, Rongzhen,Jin, Wuyin,et al. Intelligent fault diagnosis of rolling bearings using a semi-supervised convolutional neural network[J]. Applied Intelligence,2021,51(4):2144-2160.
APA Wu, Yaochun,Zhao, Rongzhen,Jin, Wuyin,He, Tianjing,Ma, Sencai,&Shi, Mingkuan.(2021).Intelligent fault diagnosis of rolling bearings using a semi-supervised convolutional neural network.Applied Intelligence,51(4),2144-2160.
MLA Wu, Yaochun,et al."Intelligent fault diagnosis of rolling bearings using a semi-supervised convolutional neural network".Applied Intelligence 51.4(2021):2144-2160.
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