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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 10
3044-3052

Fault diagnosis method via contrastive learning and neighborhood sample analysis under label noise

Jin Zezhong
Ye Chunming
Business School, University of Shanghai for Science & Technology, Shanghai 200093, China
DOI: 10.19734/j.issn.1001-3695.2024.02.0036

Abstract

Nowadays due to the dependence of fault diagnosis method based on deep learning on well-labeled training dataset, which will lead to the problem that deep neural network can easily overfit those noisy labels and affect the generalization of network under the condition of label noise. In order to achieve accurate recognition of equipment operating conditions in the network trained with label noise, this paper proposed a fault diagnosis method via contrastive learning and neighborhood sample analysis. Firstly, the method used contrastive learning to pre-train the model, which could reduce the embedding distance of similar samples in the feature space and achieved improving the ability of optimizing the feature representation ability of the network. Then, the method utilized the feature similarity to find each sample's closest neighbors to estimate the reliability of training labels which could separate all training samples into a clean or noisy subset and implemented label correction on noisy subset. After that, it established a more reliable training subset. Lastly, the proposed method made use of label reweighting and consistency regularization to enhance robustness of network. In particular, two networks got trained simultaneously where each network used the dataset division from the other network during the training process, which could mitigate confirmation bias caused by single network model training framework. The experimental results on public dataset demonstrate that proposed method can verify and correct the noisy labels impressively well and maintain great fault diagnosis performance under the condition of high-level noisy labels.

Foundation Support

上海市哲学社会科学一般项目(2022BGL010)
国家自然科学基金资助项目(71840003)
上海市科学技术委员会“科技创新行动计划”软科学重点项目(20692104300)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.02.0036
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 10
Section: Algorithm Research & Explore
Pages: 3044-3052
Serial Number: 1001-3695(2024)10-023-3044-09

Publish History

[2024-07-04] Accepted Paper
[2024-10-05] Printed Article

Cite This Article

金泽中, 叶春明. 标签噪声下结合对比学习与邻域样本分析的故障诊断方法 [J]. 计算机应用研究, 2024, 41 (10): 3044-3052. (Jin Zezhong, Ye Chunming. Fault diagnosis method via contrastive learning and neighborhood sample analysis under label noise [J]. Application Research of Computers, 2024, 41 (10): 3044-3052. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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