A Comparative Study for the Detection of Stator Inter-Turn Faults in Induction Motors Using Shallow Neural Networks and Non-Neural Based Techniques

Priynka Sharma, G. Cirrincione, Rahul Kumar, Alireza Mohammadi, Maurizio Cirrincione
{"title":"A Comparative Study for the Detection of Stator Inter-Turn Faults in Induction Motors Using Shallow Neural Networks and Non-Neural Based Techniques","authors":"Priynka Sharma, G. Cirrincione, Rahul Kumar, Alireza Mohammadi, Maurizio Cirrincione","doi":"10.1109/IC_ASET58101.2023.10150774","DOIUrl":null,"url":null,"abstract":"Traditional Induction Motor (IM) Fault Diagnosis (FD) relies on extracting features from original signals, which directly impacts FD performance. However, high-quality features require expert knowledge and human interaction. Therefore, this paper presents a comparative analysis of the detection of stator inter-turn faults in IMs using state-of-art classifiers. Experimental data was acquired and the class was divided, into up to three levels of severity for the stator fault. Thereafter, using a non-parametric approach, features from the three-phase currents were engineered relative to the characteristic fault frequencies for stator faults. Various families of classifiers were trained on the developed feature-set, and for each family of classifiers, Bayesian Optimization was applied to train its own variants and derive the best hyperparameters. The best results were obtained for Tree, Ensembled and Shallow Neural-based techniques using a separate test set.","PeriodicalId":272261,"journal":{"name":"2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IC_ASET58101.2023.10150774","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Traditional Induction Motor (IM) Fault Diagnosis (FD) relies on extracting features from original signals, which directly impacts FD performance. However, high-quality features require expert knowledge and human interaction. Therefore, this paper presents a comparative analysis of the detection of stator inter-turn faults in IMs using state-of-art classifiers. Experimental data was acquired and the class was divided, into up to three levels of severity for the stator fault. Thereafter, using a non-parametric approach, features from the three-phase currents were engineered relative to the characteristic fault frequencies for stator faults. Various families of classifiers were trained on the developed feature-set, and for each family of classifiers, Bayesian Optimization was applied to train its own variants and derive the best hyperparameters. The best results were obtained for Tree, Ensembled and Shallow Neural-based techniques using a separate test set.
浅层神经网络与非神经网络检测异步电动机定子匝间故障的比较研究
传统的异步电动机故障诊断依赖于从原始信号中提取特征,这直接影响了异步电动机的性能。然而,高质量的特性需要专业知识和人工交互。因此,本文对现有分类器在IMs定子匝间故障检测中的应用进行了对比分析。获得实验数据,并将定子故障的严重程度划分为三个级别。然后,使用非参数方法,设计了三相电流的特征,相对于定子故障的特征故障频率。在开发的特征集上训练各种分类器族,并对每个分类器族应用贝叶斯优化来训练自己的变体并获得最佳超参数。使用单独的测试集,Tree、ensembles和Shallow Neural-based技术获得了最好的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信