Title :
Fault forecast method based on particle filter
Author :
Zhou Kaijun ; Yu Lingli
Author_Institution :
Sch. of Inf. Sci. & Eng., Central South Univ., Changsha, China
Abstract :
Fault prediction based on particle filter approach is designed for dead reckoning investigation hybrid system, it utilizes a group of weighted particles to evaluate the system state, meanwhile, the fault state distribution and fault probability density distribution are calculated. Therefore, we can predict the fault probability and the fault type, furthermore, the broken-down time step can be assessed. The experimental results show that fault prediction based on particle filter can estimate the fault type for dead reckoning investigation hybrid system effectively.
Keywords :
fault diagnosis; forecasting theory; particle filtering (numerical methods); statistical distributions; dead reckoning investigation hybrid system; fault forecast method; fault prediction; fault probability density distribution; fault probability prediction; fault state distribution; particle filter; Dead reckoning; Educational institutions; Electronic mail; Fault diagnosis; Fault tolerance; Information science; Particle filters; Dead Reckoning Investigation System; Fault Forecast; Particle Filter;
Conference_Titel :
Control Conference (CCC), 2011 30th Chinese
Conference_Location :
Yantai
Print_ISBN :
978-1-4577-0677-6
Electronic_ISBN :
1934-1768