{"title":"A novel bearing fault diagnosis method for compound defects via zero-shot learning","authors":"Nguyen Duc Thuan","doi":"10.1007/s12206-024-0801-x","DOIUrl":null,"url":null,"abstract":"<p>In recent years, deep learning-based bearing fault diagnosis methods have made significant achievements. However, these methods only work with single faults and cannot diagnose compound faults because compound fault data is often unavailable in practice. To address this problem, this paper proposes a zero-shot learning-based bearing fault diagnosis method for compound defects. The proposed method utilizes an autoencoder network to observe the attributes of single faults and then estimates the attributes of compound faults. Afterward, a mapping from the data space to the attribute space is established to predict the attribute output of the data. The attribute output is then compared with prior attributes to determine the type of bearing fault. Verification experiments were conducted on HUST bearing dataset. The experimental results showed that the proposed method achieved a high accuracy of 75.64 % in diagnosing compound bearing faults.</p>","PeriodicalId":16235,"journal":{"name":"Journal of Mechanical Science and Technology","volume":"42 1","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Mechanical Science and Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s12206-024-0801-x","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
引用次数: 0
Abstract
In recent years, deep learning-based bearing fault diagnosis methods have made significant achievements. However, these methods only work with single faults and cannot diagnose compound faults because compound fault data is often unavailable in practice. To address this problem, this paper proposes a zero-shot learning-based bearing fault diagnosis method for compound defects. The proposed method utilizes an autoencoder network to observe the attributes of single faults and then estimates the attributes of compound faults. Afterward, a mapping from the data space to the attribute space is established to predict the attribute output of the data. The attribute output is then compared with prior attributes to determine the type of bearing fault. Verification experiments were conducted on HUST bearing dataset. The experimental results showed that the proposed method achieved a high accuracy of 75.64 % in diagnosing compound bearing faults.
期刊介绍:
The aim of the Journal of Mechanical Science and Technology is to provide an international forum for the publication and dissemination of original work that contributes to the understanding of the main and related disciplines of mechanical engineering, either empirical or theoretical. The Journal covers the whole spectrum of mechanical engineering, which includes, but is not limited to, Materials and Design Engineering, Production Engineering and Fusion Technology, Dynamics, Vibration and Control, Thermal Engineering and Fluids Engineering.
Manuscripts may fall into several categories including full articles, solicited reviews or commentary, and unsolicited reviews or commentary related to the core of mechanical engineering.