Title :
Failure prognosability of stochastic discrete event systems
Author :
Jun Chen ; Kumar, Ravindra
Author_Institution :
Dept. of Electr. & Comput. Eng., Iowa State Univ., Ames, IA, USA
Abstract :
We study the prognosis of fault, i.e., its prediction prior to its occurrence, in stochastic discrete event systems. We introduce the notion of m-steps Stochastic-Prognosability, called Sm-Prognosability, which allows the prediction of a fault at least m-steps in advance. We formalize the notion of a prognoser and also show that Sm-Prognosability is necessary and sufficient for the existence of a prognoser that can predict a fault at least m-steps prior to occurrence, while achieving any arbitrary false alarm and missed detection rates. We also provide a polynomial algorithm for the verification of Sm-Prognosability.
Keywords :
discrete event systems; failure analysis; fault diagnosis; polynomials; stochastic systems; Sm-prognosability; failure prognosability; false alarm; fault prediction; fault prognosis; m-steps stochastic-prognosability; missed detection rates; polynomial algorithm; prognoser; stochastic discrete event systems; Automata; Delays; Discrete-event systems; Polynomials; Prognostics and health management; Stochastic processes; Automata; Discrete event systems; Fault detection/accommodation;
Conference_Titel :
American Control Conference (ACC), 2014
Conference_Location :
Portland, OR
Print_ISBN :
978-1-4799-3272-6
DOI :
10.1109/ACC.2014.6858775