Title :
Stochastic Failure Prognosability of Discrete Event Systems
Author :
Jun Chen ; Kumar, Ratnesh
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. Finally, we compare the notion of stochastic prognosability with that of stochastic diagnosability, and show that the former is a stronger notion, as can be expected.
Keywords :
computational complexity; discrete event systems; failure analysis; fault diagnosis; reliability theory; stochastic systems; Sm-prognosability; false alarm; fault prediction; fault prognosis; m-steps stochastic-prognosability; missed detection rates; polynomial algorithm; stochastic diagnosability; stochastic discrete event systems; stochastic failure prognosability; Automata; Discrete-event systems; Polynomials; Prediction algorithms; Prognostics and health management; Stochastic processes; Discrete event systems (DESs); Stochastic prognosability; discrete event systems (DESs); failure prognosis; likelihood; stochastic prognosability;
Journal_Title :
Automatic Control, IEEE Transactions on
DOI :
10.1109/TAC.2014.2381437