{"title":"Intelligent fault diagnosis based on improved convolutional neural network for small sample and imbalanced bearing data","authors":"Xuetao Liu, Hongyan Yang","doi":"10.1177/01423312241239413","DOIUrl":null,"url":null,"abstract":"The failure of bearings is a prevalent cause of machinery breakdowns. The rapid development of intelligent technology has significantly promoted the use of deep learning techniques for identifying problems with machinery bearings. To achieve accuracy, deep learning-based diagnostic techniques require a substantial and uniformly diversified amount of training data. However, obtaining artificial labels for bearing fault data poses a major obstacle in engineering practice. This paper proposes an intelligent fault diagnosis method for bearings based on an improved convolutional neural network (CNN) to address the challenges of small training data and imbalanced distribution. To enable intelligent diagnosis of bearings with a small sample and imbalanced distribution, a clustering loss layer is introduced into the CNN. Furthermore, we optimize the parameters of the CNN by utilizing back-propagation of both the clustering loss function and the cross-entropy loss function. This optimization process improves the accuracy of fault diagnosis. Finally, the proposed method is applied to diagnose bearing faults and analyze the simulation results. The simulation results demonstrate the effectiveness of the method in handling small data volumes and imbalanced data distributions, as well as its strong generalization performance.","PeriodicalId":49426,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transactions of the Institute of Measurement and Control","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1177/01423312241239413","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
The failure of bearings is a prevalent cause of machinery breakdowns. The rapid development of intelligent technology has significantly promoted the use of deep learning techniques for identifying problems with machinery bearings. To achieve accuracy, deep learning-based diagnostic techniques require a substantial and uniformly diversified amount of training data. However, obtaining artificial labels for bearing fault data poses a major obstacle in engineering practice. This paper proposes an intelligent fault diagnosis method for bearings based on an improved convolutional neural network (CNN) to address the challenges of small training data and imbalanced distribution. To enable intelligent diagnosis of bearings with a small sample and imbalanced distribution, a clustering loss layer is introduced into the CNN. Furthermore, we optimize the parameters of the CNN by utilizing back-propagation of both the clustering loss function and the cross-entropy loss function. This optimization process improves the accuracy of fault diagnosis. Finally, the proposed method is applied to diagnose bearing faults and analyze the simulation results. The simulation results demonstrate the effectiveness of the method in handling small data volumes and imbalanced data distributions, as well as its strong generalization performance.
期刊介绍:
Transactions of the Institute of Measurement and Control is a fully peer-reviewed international journal. The journal covers all areas of applications in instrumentation and control. Its scope encompasses cutting-edge research and development, education and industrial applications.