An approach of faults estimation in Takagi-Sugeno fuzzy systems

International audienceIn this work, the problem of fault detection and identification in systems described by Takagi-Sugeno fuzzy systems is studied. A proportional integral observer is conceived in order to reconstruct state and faults which can affect the system. In order to estimate actuator and sensor faults, a mathematical transformation is made to conceive an augmented system, in which the initial sensor fault appears as an unknown input. Considering actuator fault as an unknown input, one can use a method of estimation of unknown inputs. The noise effect on the state and fault estimation is also minimized

Adaptive observer for fault estimation in nonlinear systems described by a Takagi-Sugeno model

International audienceThis paper deals with the problem of fault estimation for linear and nonlinear systems. An adaptive proportional integral observer is designed to estimate both the system state and sensor and actuator faults which can affect the system. The model of the system is first augmented in such a manner that the original sensor faults appear as actuator faults in this new model. The faults are then considered as unknown inputs and are estimated using a classical proportional-integral observer. The proposed method is first developed for linear systems and is then extended to nonlinear ones that can be represented by a Takagi-Sugeno model. In the two cases, examples of low dimensions illustrate the effectiveness of the proposed method

State and unknown input estimation via a proportional integral observer with unknown inputs

International audienceThis paper deals with the problem of fault detection and identification in noisy systems. A proportionnal integral observer with unknown inputs is used in order to estimate the state and the faults which are assumed as unknown inputs. The noise effect on the state and fault estimation errors is also minimized. The obtained results are then extended to nonlinear systems described by nonlinear Takagi-Sugeno models

State and sensor faults estimation via a proportional integral observer

International audienceThis paper deals with the problem of fault detection and identification in noisy systems. A proportional integral observer with unknown inputs is used in order to reconstruct state and sensors faults. A mathematical transformation is made to conceive an augmented system, in which the initial sensor fault appear as an unknown input. This reconstruction is made by the use of a proportionnel integral observer. The noise effect on the state and fault estimation errors is also minimized. The obtained results are then extended to nonlinear systems described by nonlinear Takagi-Sugeno models

VPCA-based fault diagnosis of spacecraft reaction wheels

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Fault diagnosis of spacecraft reaction wheels based on principal component analysis

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