{"title":"弹性状态评估监测系统","authors":"H. Garcia, Wen-Chiao Lin, S. Meerkov","doi":"10.1109/ISRCS.2012.6309301","DOIUrl":null,"url":null,"abstract":"An architecture and supporting methods are presented for the implementation of a resilient condition assessment monitoring system that can adaptively accommodate both cyber and physical anomalies to a monitored system under observation. In particular, the architecture includes three layers: information, assessment, and sensor selection. The information layer estimates probability distributions of process variables based on sensor measurements and assessments of the quality of sensor data. Based on these estimates, the assessment layer then employs probabilistic reasoning methods to assess the plant health. The sensor selection layer selects sensors so that assessments of the plant condition can be made within desired time periods. Resilient features of the developed system are then illustrated by simulations of a simplified power plant model, where a large portion of the sensors are under attack.","PeriodicalId":227062,"journal":{"name":"2012 5th International Symposium on Resilient Control Systems","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2012-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"20","resultStr":"{\"title\":\"A resilient condition assessment monitoring system\",\"authors\":\"H. Garcia, Wen-Chiao Lin, S. Meerkov\",\"doi\":\"10.1109/ISRCS.2012.6309301\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An architecture and supporting methods are presented for the implementation of a resilient condition assessment monitoring system that can adaptively accommodate both cyber and physical anomalies to a monitored system under observation. In particular, the architecture includes three layers: information, assessment, and sensor selection. The information layer estimates probability distributions of process variables based on sensor measurements and assessments of the quality of sensor data. Based on these estimates, the assessment layer then employs probabilistic reasoning methods to assess the plant health. The sensor selection layer selects sensors so that assessments of the plant condition can be made within desired time periods. Resilient features of the developed system are then illustrated by simulations of a simplified power plant model, where a large portion of the sensors are under attack.\",\"PeriodicalId\":227062,\"journal\":{\"name\":\"2012 5th International Symposium on Resilient Control Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-09-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"20\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 5th International Symposium on Resilient Control Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISRCS.2012.6309301\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 5th International Symposium on Resilient Control Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISRCS.2012.6309301","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A resilient condition assessment monitoring system
An architecture and supporting methods are presented for the implementation of a resilient condition assessment monitoring system that can adaptively accommodate both cyber and physical anomalies to a monitored system under observation. In particular, the architecture includes three layers: information, assessment, and sensor selection. The information layer estimates probability distributions of process variables based on sensor measurements and assessments of the quality of sensor data. Based on these estimates, the assessment layer then employs probabilistic reasoning methods to assess the plant health. The sensor selection layer selects sensors so that assessments of the plant condition can be made within desired time periods. Resilient features of the developed system are then illustrated by simulations of a simplified power plant model, where a large portion of the sensors are under attack.