Abstract
This chapter provides solutions to the fault detection, isolation and estimation problems for systems described by deterministic continuous-variable models. The chapter considers faults that can be modelled as additive signals acting on the process. The methods presented lead to a diagnostic system which is separated in two parts: a residual generation module and a residual evaluation module.
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Notes
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Again the same functions g and h as above are used by an abuse of notation.
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This notion should not be confused with the detectability of a linear system or a pair (C, A); indeed, the latter notion depends on the map from state to measured output, while the fault detectability is an input(i.e. fault)/output(i.e. residual) property.
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The notion of rank considered here is the normal rank computed as \(\max _s \;\mathrm{rank} \;H(s)\) where the maximum is taken over all complex values of s.
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The Maple symbolic mathematics engine is a stand-alone product. It is also a part of the MATLAB Symbolic Toolbox. MATLAB\(^\circledR \), Maple\(^\circledR \) and Mathematica\(^\circledR \) are registered trademarks of their respective owners.
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© 2016 Springer-Verlag Berlin Heidelberg
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Blanke, M., Kinnaert, M., Lunze, J., Staroswiecki, M. (2016). Fault Diagnosis of Deterministic Systems. In: Diagnosis and Fault-Tolerant Control. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47943-8_6
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DOI: https://doi.org/10.1007/978-3-662-47943-8_6
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-47942-1
Online ISBN: 978-3-662-47943-8
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