Introducing Dynamics in a Fault Diagnostic Application Using Bayesian Belief Networks
Fault diagnostic techniques are required to determine whether a fault has occurred in a system and to identify the component failures that may have caused it.This task can be complicated when dealing with complex systems and dynamic behaviour,in particular,introduces further difficulties.This paper presents a method for fault detection on dynamic systems using Baycsian Belief Networks (BBNs).Possible trends are identified for the variables in the systems that are monitored by the sensors.Fault Trees (FTs) are built to represent the causality of the trends and these arc then converted into BBNs.The networks developed for different sections are connected together to form a unique concise network.For a combination of sensors which deviate from the expected trends,calculating the updated probability enables a list of potential causes for the system scenarios to be obtained.A simple water tank system has been used to validate the method.
Fault Diagnostics Bayesian Belief Networks Fault Tree Analysis
Mariapia Lampis John Andrews
Aeronautical and Automotive Engineering Department Loughborough University Loughborouglt,UK Aeronautical and Automotive Engineering Department Loughborough University Loughborough,UK
国际会议
2009 8th International Conference on Reliability,Maintainability and Safety(第八届中国国际可靠性、维修性、安全性会议)
成都
英文
186-190
2009-08-24（万方平台首次上网日期，不代表论文的发表时间）