In a system’s design phase, where knowledge about the actual behavior of the system is shallow, the design of an efficient and robust system diagnostics is a complex task. In order to simplify this process, this paper presents a modelbased methodology for the design of fault diagnosis schemes. The methodology analyzes the structure of available behavioral models of the system and proposes minimal sets of sensors to fulfill diagnostic requirements. In order to choose an optimal set of sensors, the method evaluates the sets in terms of costs and diagnostic robustness. The proposed fault detection, isolation and identification schemes rely on the robust evaluation of model-based residuals using Monte-Carlo methods and highest density regions to account for measurement and parameter uncertainty. To show the design capabilities, the presented method is applied to an aircraft hydraulic power package and the resulting schemes are tested on a real test rig.
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Fault Diagnosis, Model-Based, Robustness, Aircraft Hydraulic Power Package
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