基于领域知识融合Word2vec的风机告警序列聚类分析

IF 2.5 4区 综合性期刊 Q2 CHEMISTRY, MULTIDISCIPLINARY
Lu Wei, Liliang Wang, Feng Liu, Zheng Qian
{"title":"基于领域知识融合Word2vec的风机告警序列聚类分析","authors":"Lu Wei, Liliang Wang, Feng Liu, Zheng Qian","doi":"10.3390/app131810114","DOIUrl":null,"url":null,"abstract":"The alarm data contain abundant fault information related to almost all components of the wind turbine. Reasonable analysis and utilization of alarm data can assist wind farm maintenance personnel in quickly identifying the types of turbine faults, reducing operation and maintenance costs. This paper proposes a clustering analysis method that groups similar alarm sequences with the same fault type. Firstly, the alarm data are preprocessed, where alarm sequences are segmented, and redundant alarms are removed. Then, a domain knowledge-fused Word2vec (DK-Wrod2vec) method is introduced to transform non-numeric alarm codes into numeric vector representations. Finally, new distance metrics are incorporated into the K-means clustering algorithm to improve clustering performance. The performance of the proposed clustering method is assessed by applying it to labeled alarm sequences. The results demonstrate that the clustering performance is the best when using DK-Word2vec and the word rotator’s distance compared with other methods. Additionally, with the optimal parameter combination, the fault types of unlabeled alarm sequences are also analyzed.","PeriodicalId":48760,"journal":{"name":"Applied Sciences-Basel","volume":" ","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Clustering Analysis of Wind Turbine Alarm Sequences Based on Domain Knowledge-Fused Word2vec\",\"authors\":\"Lu Wei, Liliang Wang, Feng Liu, Zheng Qian\",\"doi\":\"10.3390/app131810114\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The alarm data contain abundant fault information related to almost all components of the wind turbine. Reasonable analysis and utilization of alarm data can assist wind farm maintenance personnel in quickly identifying the types of turbine faults, reducing operation and maintenance costs. This paper proposes a clustering analysis method that groups similar alarm sequences with the same fault type. Firstly, the alarm data are preprocessed, where alarm sequences are segmented, and redundant alarms are removed. Then, a domain knowledge-fused Word2vec (DK-Wrod2vec) method is introduced to transform non-numeric alarm codes into numeric vector representations. Finally, new distance metrics are incorporated into the K-means clustering algorithm to improve clustering performance. The performance of the proposed clustering method is assessed by applying it to labeled alarm sequences. The results demonstrate that the clustering performance is the best when using DK-Word2vec and the word rotator’s distance compared with other methods. Additionally, with the optimal parameter combination, the fault types of unlabeled alarm sequences are also analyzed.\",\"PeriodicalId\":48760,\"journal\":{\"name\":\"Applied Sciences-Basel\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2023-09-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Sciences-Basel\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.3390/app131810114\",\"RegionNum\":4,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Sciences-Basel","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.3390/app131810114","RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 1

摘要

警报数据包含与风力涡轮机的几乎所有部件相关的丰富的故障信息。合理分析和利用报警数据可以帮助风电场维护人员快速识别风机故障类型,降低运行和维护成本。本文提出了一种聚类分析方法,将具有相同故障类型的相似报警序列进行分组。首先,对报警数据进行预处理,对报警序列进行分段,去除冗余报警。然后,引入了一种领域知识融合的Word2vec(DK-Wrod2vec)方法,将非数字报警代码转换为数字矢量表示。最后,在K-means聚类算法中引入了新的距离度量,以提高聚类性能。通过将所提出的聚类方法应用于标记的警报序列来评估其性能。结果表明,与其他方法相比,使用DK-Word2vec和单词旋转器距离的聚类性能最好。此外,在最优参数组合下,还分析了未标记报警序列的故障类型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Clustering Analysis of Wind Turbine Alarm Sequences Based on Domain Knowledge-Fused Word2vec
The alarm data contain abundant fault information related to almost all components of the wind turbine. Reasonable analysis and utilization of alarm data can assist wind farm maintenance personnel in quickly identifying the types of turbine faults, reducing operation and maintenance costs. This paper proposes a clustering analysis method that groups similar alarm sequences with the same fault type. Firstly, the alarm data are preprocessed, where alarm sequences are segmented, and redundant alarms are removed. Then, a domain knowledge-fused Word2vec (DK-Wrod2vec) method is introduced to transform non-numeric alarm codes into numeric vector representations. Finally, new distance metrics are incorporated into the K-means clustering algorithm to improve clustering performance. The performance of the proposed clustering method is assessed by applying it to labeled alarm sequences. The results demonstrate that the clustering performance is the best when using DK-Word2vec and the word rotator’s distance compared with other methods. Additionally, with the optimal parameter combination, the fault types of unlabeled alarm sequences are also analyzed.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Applied Sciences-Basel
Applied Sciences-Basel CHEMISTRY, MULTIDISCIPLINARYMATERIALS SCIE-MATERIALS SCIENCE, MULTIDISCIPLINARY
CiteScore
5.30
自引率
11.10%
发文量
10882
期刊介绍: Applied Sciences (ISSN 2076-3417) provides an advanced forum on all aspects of applied natural sciences. It publishes reviews, research papers and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. Electronic files and software regarding the full details of the calculation or experimental procedure, if unable to be published in a normal way, can be deposited as supplementary electronic material.
×
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