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 |
ISSN | 0924-669X |
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 |
DOI | 10.1007/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 | |
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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