ENBIS-8 in Athens
21 – 25 September 2008
Abstract submission: 14 March – 11 August 2008
Design of fault diagnosis systems: comparing a method based on robust design with classical control engineering approaches
24 September 2008, 09:00 – 09:20
- Submitted by
- Daniele Romano
- Daniele Romano and Michel Kinnaert
- University of Cagliari, Cagliari, Italy; Université Libre de Bruxelles, Bruxelles, Belgium
- Fault Detection and Isolation (FDI) is an important topic in control engineering since the seventies. FDI systems provide a precocious diagnosis of failures or malfunctions which may occur on a dynamic system by elaborating signals coming from on-line monitoring. Typical applications are in the process industry, in transportation and telecommunications, where the supervised systems may be industrial plants and critical sub-systems of aircrafts, trains, space shuttles, and satellites. Prompt detection of the fault and early understanding of its cause (isolation) can prevent the product from loss of quality, the plant from unexpected shutdown and, more importantly, the environment and the humans from harmful effects in case of failures during hazardous operations.
The basic diagnostic mechanism is to compute a set of fault indicators and then assess their current value against some reference values in order to make the diagnosis decision. The reference values are obtained by means of previously identified models of the system behaviour under both sound and faulty conditions (model-based FDI). As different sources of noise affect the system (random deviations from the nominal set-point, uncertainty in model parameters, variation of the fault level, measurement error of sensors) the design of faults indicators can be regarded as a kind of robust design problem with the objectives to minimise the rate of false alarms, missed detection and missed isolation. For solving such a problem the authors developed a method based on the Kullback divergence as a measure for obtaining the maximum decoupling among indicators of different faults.
Using a fluid mixing process as a test case, we show that this approach is superior to two of the most known methods in the control engineering literature. One of them is a model-based method (Parity Space), the other is a data-based one (Fisher Discriminant Analysis).
CHOW, E.Y., and WILLSKY, A.S., 1984, “Analytical Redundancy and the Design of Robust Failure Detection Systems”. IEEE Trans. on Automatic Control, AC-29(7), 603-614.
ROMANO D., and KINNAERT M., (2006), “Robust Design of Fault Detection and Isolation Systems”. Quality Reliability Engineering International, 22(5), 527-538.
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