Data-Driven and Adaptive Statistical Residual Evaluation for Fault Detection with an Automotive Application
An important step in model-based fault detection is residual
evaluation, where residuals are evaluated with the aim to detect
changes in their behavior caused by faults. To handle residuals
subject to time-varying uncertainties and disturbances, which indeed
are present in practice, a novel statistical residual evaluation
approach is presented. The main contribution is to base the residual
evaluation on an explicit comparison of the probability distribution
of the residual, estimated online using current data, with a no-fault
residual distribution. The no-fault distribution is based on a set of
a-priori known no-fault residual distributions, and is continuously
adapted to the current situation. As a second contribution, a method
is proposed for estimating the required set of no-fault residual
distributions off-line from no-fault training data. The proposed
residual evaluation approach is evaluated with measurement data on a
residual for fault detection in the gas-flow system of a Scania truck
diesel engine. Results show that small faults can be reliable detected
with the proposed approach in cases where regular methods fail.
Carl Svärd, Mattias Nyberg, Erik Frisk and Mattias Krysander
Mechanical Systems and Signal Processing,
2014
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