Feed-forward observer-based intermittent fault detection

This paper provided an approach to design feed-forward observer for nonlinear systems with Lipchitz nonlinearity and bounded unknown inputs (disturbances/uncertainties) to ensure the sensitivity against intermittent faults. The proposed observer design guarantees the system error stability. Some variables and scalars are also introduced to design observer's parameters, which bring more degrees of flexibility available to the designer. The designed observer is used to propose a precision fault detection scheme including adaptive threshold design to detect intermittent faults. The efficiency of the considered approach is examined by the intermittent failure case in the suspension system of a vehicle. Simulation results show that the accurate state estimation and fault detection are achieved successfully

Online Sensor Fault Detection Based on an Improved Strong Tracking Filter

We propose a method for online sensor fault detection that is based on the evolving Strong Tracking Filter (STCKF). The cubature rule is used to estimate states to improve the accuracy of making estimates in a nonlinear case. A residual is the difference in value between an estimated value and the true value. A residual will be regarded as a signal that includes fault information. The threshold is set at a reasonable level, and will be compared with residuals to determine whether or not the sensor is faulty. The proposed method requires only a nominal plant model and uses STCKF to estimate the original state vector. The effectiveness of the algorithm is verified by simulation on a drum-boiler model

Online Sensor Fault Detection Based on an Improved Strong Tracking Filter

We propose a method for online sensor fault detection that is based on the evolving Strong Tracking Filter (STCKF). The cubature rule is used to estimate states to improve the accuracy of making estimates in a nonlinear case. A residual is the difference in value between an estimated value and the true value. A residual will be regarded as a signal that includes fault information. The threshold is set at a reasonable level, and will be compared with residuals to determine whether or not the sensor is faulty. The proposed method requires only a nominal plant model and uses STCKF to estimate the original state vector. The effectiveness of the algorithm is verified by simulation on a drum-boiler model