Abstract
The concepts of quantitative fault diagnosis and fault tolerant control are introduced in this chapter. After a thorough introduction, the notion of analytical redundancy relations (ARRs), residuals, and structural analysis for fault diagnosis and isolation (FDI) are presented. Causal structure of bond graph model is exploited to derive the ARRs which are analyzed in real-time for FDI. Fault accommodation is performed through system reconfiguration so that suitably chosen redundant devices are activated in place of faulty components. Such management of operating modes is handled through a well-developed algorithm based on availability of healthy components and their functional associations to achieve the desired objectives. An example application concerning actuator failure in an electric vehicle is considered. In the next step, diagnosis of uncertain parameter systems is discussed where uncertainties are included in the ARRs so that false alarms and misdetections can be avoided. Parametric uncertainties are modeled through linear fractional transformation in bond graph form and bounding adaptive thresholds are derived for residual signals. This approach leads to robust diagnosis of uncertain systems which is demonstrated through an example mechatronic system application.
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This example is taken from these authors’ previous work published in [73].
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Merzouki, R., Samantaray, A.K., Pathak, P.M., Ould Bouamama, B. (2013). Model-Based Fault Diagnosis and Fault Tolerant Control. In: Intelligent Mechatronic Systems. Springer, London. https://doi.org/10.1007/978-1-4471-4628-5_7
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