Title :
Statistical properties of exponentially weighted moving average algorithm for change detection
Author :
Chitraganti, Shaikshavali ; Aberkane, S. ; Aubrun, Christophe
Author_Institution :
Centre de Rech. en Autom. de Nancy (CRAN), Univ. de Lorraine, Vandoeuvre-les-Nancy, France
Abstract :
In this paper, the statistical properties of a change detection algorithm are considered. More specifically, we considered the exponentially weighted moving average (EWMA) algorithm. Analytical expressions for the probability distribution of detection delay and the time between false alarms are proposed, and the results are validated by simulations. The results can be used in examining the abrupt changes in a signal, and also in the design of active fault tolerant control systems.
Keywords :
control system synthesis; delays; fault tolerance; moving average processes; statistical distributions; AFTCS design; EWMA algorithm; active fault tolerant control systems; change detection algorithm; detection delay; exponentially weighted moving average algorithm; false alarms; probability distribution; statistical properties; Delay; Detection algorithms; Fault detection; Fault tolerance; Markov processes; Probability density function; Yttrium;
Conference_Titel :
Decision and Control (CDC), 2012 IEEE 51st Annual Conference on
Conference_Location :
Maui, HI
Print_ISBN :
978-1-4673-2065-8
Electronic_ISBN :
0743-1546
DOI :
10.1109/CDC.2012.6426477
