A Data-Driven and Probabilistic Approach to Residual Evaluation for Fault Diagnosis
An important step in fault detection and isolation is residual
evaluation where residuals, signals ideally zero in the no-fault
case, are evaluated with the aim to detect changes in their behavior
caused by faults. Generally, residuals deviate from zero even in the
no-fault case and their probability distributions exhibit
non-stationary features due to, e.g., modeling errors, measurement
noise, and different operating conditions. To handle these issues,
this paper proposes a data-driven approach to residual evaluation
based on an explicit comparison of the residual distribution
estimated on-line and a no-fault distribution, estimated off-line
using training data. The comparison is done within the framework of
statistical hypothesis testing. With the Generalized Likelihood
Ratio test statistic as starting point, a more powerful and
computational efficient test statistic is derived by a properly
chosen approximation to one of the emerging likelihood maximization
problems. The proposed approach is evaluated with measurement data
on a residual for diagnosis of the gas-flow system of a Scania truck
diesel engine. The proposed test statistic performs well, small
faults can for example be reliable detected in cases where regular
methods based on constant thresholding fail.
Carl Svärd, Mattias Nyberg, Erik Frisk and Mattias Krysander
2011
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