An application of a recently introduced framework for isolating unprecedented
faults for an automotive engine EGR valve system is presented. Using
normal behavior data generated by a high fidelity engine simulation, the Growing
Structure Multiple Model System (GSMMS) is used to construct models of normal
behavior for EGR valve system and its various subsystems. Using the GSMMS
models as a foundation, anomalous behavior of the entire system is then detected
as statistically significant departures of the most recent modeling residuals from the
modeling residuals during normal behavior. By reconnecting anomaly detectors to
the constituent subsystems, the anomaly can be isolated without the need for prior
training using faulty data. Furthermore, faults that were previously encountered
(and modeled) are recognized using the same approach as the anomaly detectors. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/ETD-UT-2009-12-610 |
| Date | 25 August 2010 |
| Creators | Cholette, Michael Edward |
| Contributors | Djurdjanovic, Dragan |
| Source Sets | University of Texas |
| Language | English |
| Detected Language | English |
| Type | thesis |
| Format | application/pdf |
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