Abstract
Abstract Sensor-rich systems typically employ extensive signal processing techniques for fault detection and isolation tasks. Sensor-poor systems, on the other hand, require system models and analytical redundancy techniques to make diagnostic inferences. The increasing availability of inexpensive, batch-fabricated micro-controllers and MEMS sensors enables deployment of a multitude of sensors and microprocessors for control and diagnosis of embedded systems. We develop a diagnosis method that combines model-based diagnosis with signal processing techniques to address the challenges in diagnosing complex systems with hybrid discrete/continuous behaviors and to reduce the computational requirements by focusing the signal processing algorithms. We demonstrate the approach on problems in reprographic copier paper path diagnosis.
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