无监督聚类故障诊断

Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing) Pub Date : 2012-05-23 DOI:10.1109/PHM.2012.6228844

P. Baraldi, F. D. Di Maio, E. Zio

{"title":"无监督聚类故障诊断","authors":"P. Baraldi, F. D. Di Maio, E. Zio","doi":"10.1109/PHM.2012.6228844","DOIUrl":null,"url":null,"abstract":"We develop an unsupervised clustering method for the classification of transient data. A fuzzy-based technique is employed to measure the similarity among the transients; a spectral clustering technique, embedding the unsupervised Fuzzy C-Means (FMC) algorithm, is applied to the matrix of similarity values so that the clusters are formed by patterns most similar to each other. The performance of the proposed technique is tested with respect to a case study with data artificially generated.","PeriodicalId":444815,"journal":{"name":"Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Unsupervised clustering for fault diagnosis\",\"authors\":\"P. Baraldi, F. D. Di Maio, E. Zio\",\"doi\":\"10.1109/PHM.2012.6228844\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We develop an unsupervised clustering method for the classification of transient data. A fuzzy-based technique is employed to measure the similarity among the transients; a spectral clustering technique, embedding the unsupervised Fuzzy C-Means (FMC) algorithm, is applied to the matrix of similarity values so that the clusters are formed by patterns most similar to each other. The performance of the proposed technique is tested with respect to a case study with data artificially generated.\",\"PeriodicalId\":444815,\"journal\":{\"name\":\"Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing)\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-05-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PHM.2012.6228844\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PHM.2012.6228844","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 11

摘要

我们开发了一种用于暂态数据分类的无监督聚类方法。采用一种基于模糊的技术来度量瞬态之间的相似度;将嵌入无监督模糊c均值(FMC)算法的谱聚类技术应用于相似值矩阵，使聚类由彼此最相似的模式组成。针对人工生成的数据进行了案例研究，测试了所提出技术的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Unsupervised clustering for fault diagnosis

We develop an unsupervised clustering method for the classification of transient data. A fuzzy-based technique is employed to measure the similarity among the transients; a spectral clustering technique, embedding the unsupervised Fuzzy C-Means (FMC) algorithm, is applied to the matrix of similarity values so that the clusters are formed by patterns most similar to each other. The performance of the proposed technique is tested with respect to a case study with data artificially generated.

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

Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing)

自引率

0.00%

发文量