{"title":"Investigation of Feature Effectiveness in Polymer Electrolyte Membrane Fuel Cell Fault Diagnosis","authors":"Weitao Pan, Y. Y. A. Abuker, L. Mao","doi":"10.1109/phm-qingdao46334.2019.8942975","DOIUrl":null,"url":null,"abstract":"This paper investigates effectiveness of various features in fault diagnosis of polymer electrolyte membrane fuel cell (PEMFC) system, including RMSF (root mean square frequency), ACSD (autocorrelation standard deviation) and kurtosis. Test data is collected from a PEMFC system with various conditions, such as normal operation, flooding and drying out scenarios. By extracting selected features from PEMFC voltage, the performance of various features in isolating PEMFC states is investigated using k-means clustering. Results demonstrate that the combination of RMSF and ACSD could provide reliable fault diagnostic performance. Moreover, kurtosis might be used as a fast diagnostic indicator for various PEMFC degradation mechanisms.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/phm-qingdao46334.2019.8942975","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
This paper investigates effectiveness of various features in fault diagnosis of polymer electrolyte membrane fuel cell (PEMFC) system, including RMSF (root mean square frequency), ACSD (autocorrelation standard deviation) and kurtosis. Test data is collected from a PEMFC system with various conditions, such as normal operation, flooding and drying out scenarios. By extracting selected features from PEMFC voltage, the performance of various features in isolating PEMFC states is investigated using k-means clustering. Results demonstrate that the combination of RMSF and ACSD could provide reliable fault diagnostic performance. Moreover, kurtosis might be used as a fast diagnostic indicator for various PEMFC degradation mechanisms.
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