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ELIMINATING THE INITIAL STATE FOR THE GENERALIZED LIKELIHOOD RATIO TEST
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BibTeX
@MISC{Gustafsson_eliminatingthe,
author = {Fredrik Gustafsson},
title = {ELIMINATING THE INITIAL STATE FOR THE GENERALIZED LIKELIHOOD RATIO TEST},
year = {}
}
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Abstract
Abstract: Fault detection based on comparing a batch of data with a model of the system using the generalized likelihood ratio test is considered. Careful treatment of the initial state of the model is quite important, in particular for short batch sizes. There are two standard approaches to this problem. One is based on a parity space, where the influence of initial state is removed by projection, and the other on using prior information obtained by Kalman filtering past data. A new idea of anti-causal Kalman filtering in the present data batch is introduced and compared to the previous methods. An efficient parameterization of incipient faults is given. It is shown in simulations of torque disturbances on a DC-motor that efficient fault profile parameterization and using smoothed estimates of the initial state increase performance considerably. Copyright c©2006 IFAC
Keyphrases
initial state generalized likelihood ratio test initial state increase performance standard approach efficient fault profile parameterization anti-causal kalman filtering short batch size past data new idea efficient parameterization careful treatment fault detection likelihood ratio test present data batch prior information smoothed estimate torque disturbance previous method parity space incipient fault
