Conference Paper
Simultaneous sensor and actuator fault reconstruction and diagnosis using generalized sliding mode observers
Dept. of Electr. & Comput. Eng., Univ. of Alberta, Edmonton, AB, Canada
Conference: American Control Conference (ACC), 2010 Source: IEEE Xplore

Conference Paper: Estimation and reconstruction of multiple system, actuator and sensor faults in timedelayed linear systems
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ABSTRACT: Detection, isolation, and reconstruction of multiple system, actuator, and sensor faults in timedelayed linear systems are considered in this note. Based on an appropriate change of system coordinates; all system, actuator, and sensor faults are assigned to different subsystems of a timedelayed linear system. Then, using sliding mode observers and equivalent output error injection approaches, all faults are reconstructed separately. This results in reduced computational complexity. Finally, numerical simulation is used to show the feasibility and effectiveness of the proposed approach.Control and Automation (ICCA), 2011 9th IEEE International Conference on; 01/2011 
Conference Paper: Robust and simultaneous reconstruction of actuator and sensor faults via sliding mode observer
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ABSTRACT: This paper proposes a method for robust and simultaneous actuator and sensor faults reconstruction of linear uncertain system based on sliding mode observer (SMO). In comparison with existing work, the observer contains two discontinuous terms to solve the problem of simultaneous faults. The idea is to introduce an appropriate filter on the systems output to transform the sensor faults in the fictitious actuator faults and hence the proposed SMO can be applied.Electrical Engineering and Software Applications (ICEESA), Hammamet; 03/2013  [Show abstract] [Hide abstract]
ABSTRACT: A multiple fault tolerant measurement system based on nonlinear dynamic models, special searching algorithm, principle components decomposition and Q test is developed. The proposed system uses a modelbased estimator to deliver symptoms. The symptoms are then analyzed in a statistical unit in order to detect the faults and isolate the faulty sensors. Multilayer perceptron networks, radial basis function networks and Tagaki–Sugeno fuzzy models were examined for the fault estimator module and among these fuzzy models presented the best performance. The main advantages of the proposed scheme are the capability to detect, isolate and repair multiple faults in both input and output sensors and the feasibility to be applied to any system with as many sensors as required, all due to particular design of its modelbased estimator. The system was tested on a CSTH model developed based on an experimental platform; different experiments demonstrated satisfactory results.Neural Computing and Applications · 1.76 Impact Factor
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