Integrating Importance Measure with Sensitivity Analysis to Improve the Reliability of Cascading Failure System

  • 顏 肇余

Student thesis: Master's Thesis

Abstract

Our research aims to build a more integrated analysis to measure the importance in a cascading failure system by combining different existing data We find out the influence between components can not be ignored in a complex system The cascading failure condition is very common in a engineering system but the common methods that analyze influence from components to system (ex:sensitivity FMEA B-reliability importance measure ) are not suit enough to deal with a black-box cascading failure system In most design cases we have limited data about the success or failure condition of some components and system from current design In order to build a more integrated analysis we use the B-reliability importance measure combine with the failure dependency matrix and sequence of failure that we called Cascading Importance Measure (CIM) Based on the limited existing data the CIM analysis is hard to completely correct in the testing result Because the CIM is measuring the importance of component reliability to system reliability and adjusting design variables to improve the components' reliability But the change of the design variable is not only change the reliability of it's own component To build a more completed analysis we use the information of sensitivity analysis from variables to each component's performance Under the data of sensitivity analysis from variables to each component's performance we can build a new method to enhance CIM (CIM+) And measure the importance from design variables to system reliability Finally we use our two method to a electrical system which is a typical cascading failure system And then verify the result of two methods by testing result of improving system reliability
Date of Award2014 Aug 30
Original languageEnglish
SupervisorJahau Lewis Chen (Supervisor)

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