GLR Tests for Fault Detection over Sliding Data Windows
| Authors: | Tornqvist David, Linkoping University, Sweden
Gustafsson Fredrik, Linkoping University, Sweden
Klein Inger, Linkoping University, Sweden |
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| Topic: | 1.1 Modelling, Identification & Signal Processing |
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| Session: | Monitoring and Change Detection |
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| Keywords: | fault detection, statistical signal processing, robust estimation, parity space |
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Abstract
The Generalized Likelihood Ratio (GLR) test for fault detection as derived by Willsky and Jones is a recursive method to detect additive changes in linear systems in a Kalman filter framework. Here, we evaluate the GLR test on a sliding window and compare it to stochastic parity space approaches. Robust fault detection defined as being insensitive to faults in the signal space is also studied in the GLR framework.
