Residual change detection using low-complexity sequential quantile estimation
Detecting changes in residuals is important for fault detection and is
commonly performed by thresholding the residual using, for example, a
CUSUM test. However, detecting variations in the residual
distribution, not causing a change of bias or increased variance, is
difficult using these methods. A plug-and-play residual change
detection approach is proposed based on sequential quantile estimation
to detect changes in the residual cumulative density function. An
advantage of the proposed algorithm is that it is non-parametric and
has low computational cost and memory usage which makes it suitable
for on-line implementations where computational power is limited.
Daniel Jung, Erik Frisk and Mattias Krysander
2017
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Last updated: 2019-12-12
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