The influence of feature vector on the classification of mechanical faults using neural networks

Denys Pestana-Viana, Rafael Zambrano-Lopez, A. Lima, T. Prego, S. L. Netto, E. Silva
{"title":"The influence of feature vector on the classification of mechanical faults using neural networks","authors":"Denys Pestana-Viana, Rafael Zambrano-Lopez, A. Lima, T. Prego, S. L. Netto, E. Silva","doi":"10.1109/LASCAS.2016.7451023","DOIUrl":null,"url":null,"abstract":"This paper investigates the problem of automatic detection of rotating-machine faults based on vibration signals acquired during machine operation. In particular, two new signal features, namely the kurtosis and entropy, are considered along with main spectral peaks to discriminate between several machine conditions: normal operation, (vertical and horizontal) misalignment, unbalanced load and bearing faults. Moreover, the inclusion of one set of three accelerometers for each roller bearing associated to the system acquiring more vibration signals also affects the generation of feature vector and is part of our proposal. In order to evaluate the rotating machine fault classification, a database of 1951 fault scenarios with several different fault intensities and rotating frequencies was designed and recorded, taking into consideration the specificities of the proposed machine learning task. The artificial neural networks recognition system employed in this work reached 95.8% of overall accuracy, showing the efficiency of the proposed approach.","PeriodicalId":129875,"journal":{"name":"2016 IEEE 7th Latin American Symposium on Circuits & Systems (LASCAS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 7th Latin American Symposium on Circuits & Systems (LASCAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/LASCAS.2016.7451023","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 19

Abstract

This paper investigates the problem of automatic detection of rotating-machine faults based on vibration signals acquired during machine operation. In particular, two new signal features, namely the kurtosis and entropy, are considered along with main spectral peaks to discriminate between several machine conditions: normal operation, (vertical and horizontal) misalignment, unbalanced load and bearing faults. Moreover, the inclusion of one set of three accelerometers for each roller bearing associated to the system acquiring more vibration signals also affects the generation of feature vector and is part of our proposal. In order to evaluate the rotating machine fault classification, a database of 1951 fault scenarios with several different fault intensities and rotating frequencies was designed and recorded, taking into consideration the specificities of the proposed machine learning task. The artificial neural networks recognition system employed in this work reached 95.8% of overall accuracy, showing the efficiency of the proposed approach.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
特征向量对神经网络机械故障分类的影响
本文研究了基于旋转机械运行过程中振动信号的故障自动检测问题。特别是,考虑了两个新的信号特征,即峰度和熵,以及主要的频谱峰,以区分几种机器状态:正常运行,(垂直和水平)不对中,不平衡负载和轴承故障。此外,为每个与系统获取更多振动信号相关的滚子轴承包含一组三个加速度计也会影响特征向量的生成,这是我们建议的一部分。为了评估旋转机械故障分类,考虑到所提出机器学习任务的特殊性,设计并记录了1951个具有不同故障强度和旋转频率的故障场景数据库。本文所采用的人工神经网络识别系统达到了95.8%的总体准确率,表明了所提方法的有效性。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Energy harvesting with 3D-printed electrostatic generators A 30dBm PA for MTC communication in 65nm CMOS technology A low-cost microcontrolled dosimeter based on CD4007 devices for in vivo radiotherapy applications Telepresence using the kinect sensor and the NAO robot DC-DC switching converter as on-field self energy meter
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1