Sara Rachidi, E. Leclercq, Yoann Pigné, D. Lefebvre
{"title":"Moving Average control chart for the detection and isolation of temporal faults in stochastic Petri nets","authors":"Sara Rachidi, E. Leclercq, Yoann Pigné, D. Lefebvre","doi":"10.1109/ETFA.2018.8502633","DOIUrl":null,"url":null,"abstract":"This paper deals with problems of detection and isolation of temporal faults in timed stochastic discrete event systems. Partially labeled timed Petri nets are used to model the considered systems. Temporal faults corresponding to significant variations of the support of the probability density function (pdf) are considered. A pdf represents the firing duration of each transition. A Moving Average control chart (also known as a Moving Mean chart) is applied in order to detect the variation of mean duration. The advantages of the proposed analysis are to detect variations in time series when parameters vary slowly and to isolate the faults thanks to the signature table.","PeriodicalId":6566,"journal":{"name":"2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA)","volume":"44 1","pages":"493-498"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ETFA.2018.8502633","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This paper deals with problems of detection and isolation of temporal faults in timed stochastic discrete event systems. Partially labeled timed Petri nets are used to model the considered systems. Temporal faults corresponding to significant variations of the support of the probability density function (pdf) are considered. A pdf represents the firing duration of each transition. A Moving Average control chart (also known as a Moving Mean chart) is applied in order to detect the variation of mean duration. The advantages of the proposed analysis are to detect variations in time series when parameters vary slowly and to isolate the faults thanks to the signature table.