Model-Based Fault Diagnosis Techniques

By S. Simani, C. Fantuzzi, R. Patton. 2003

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Survey on data-driven industrial process monitoring and diagnosis (2012, methodology): “If the next normal data sample depends on the past, the normal regions defined by the static PCA or PLS methods are often too large, leading to high rate of missed detections or Type II errors. Dynamic extensions for data modeling and monitoring such as Ku et al. (1995), Qin and McAvoy (1996), Li an”
LMI solutions to the mixed ℋ︁−/ℋ︁∞ fault detection observer design for linear parameter‐varying systems (2011, methodology): “Nevertheless, finding systematic design methods for systems subjected to unknown disturbances and model uncertainties has been proven to be difficult [5, 13].”