Residual Selection for Consistency Based Diagnosis Using Machine Learning Models
A common architecture of model-based diagnosis systems is to use a
set of residuals to detect and isolate faults. In the paper it is
motivated that in many cases there are more possible candidate
residuals than needed for detection and single fault isolation and
key sources of varying performance in the candidate residuals are
model errors and noise. This paper formulates a systematic method of
how to select, from a set of candidate residuals, a subset with good
diagnosis performance. A key contribution is the combination of a
machine learning model, here a random forest model, with diagnosis
specific performance specifications to select a high performing
subset of residuals. The approach is applied to an industrial use
case, an automotive engine, and it is shown how the trade-off
between diagnosis performance and the number of residuals easily can
be controlled. The number of residuals used are reduced from
original 42 to only 12 without losing significant diagnosis
performance.
Erik Frisk and Mattias Krysander
2018
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