Diagnosing and correcting system anomalies with a robust classifier
- 24 December 2002
- proceedings article
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 6, 3506-3508
- https://doi.org/10.1109/icassp.1996.550784
Abstract
If a robust statistical model has been developed to classify the "health" of a system, a well-known Taylor series approximation technique forms the basis of a diagnostic/recovery procedure that can be initiated when the system's health degrades or fails altogether. This procedure determines a ranked set of probable causes for the degraded health state, which can be used as a prioritized checklist for isolating system anomalies and quantifying corrective action. The diagnostic/recovery procedure is applicable to any classifier known to be robust; it can be applied to both neural network and traditional parametric pattern classifiers generated by a supervised learning procedure in which an empirical risk/benefit measure is optimized. We describe the procedure mathematically and demonstrate its ability to detect and diagnose the cause(s) of faults in NASA's Deep Space Communications Complex at Goldstone, California.Keywords
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