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- W1977194837 abstract "This paper presents a Fault Detection and Diagnosis (FDD) method for flight parameter sensors of an aircraft. In the most general case, fault detection of these sensors on modern aircraft is performed by a logic that selects one out of three redundant measurements. Such a method is compliant with current airworthiness regulations. However, in the framework of the global aircraft optimization for future and upcoming aircraft, it could be required, e.g. to extend the availability of sensor measurements. So, an improvement of the state of practice could be useful. Introducing a form of analytical redundancy of these measurements can increase the fault detection performance and result in a weight reduction of the aircraft because there is no necessity anymore for the redundant hardware. The analytical redundancy in this method is introduced using an adaptive form of the Extended Kalman Filter (EKF). This EKF uses the kinematic relations of the aircraft and makes a state reconstruction from the available measurements possible. From this estimated state, an estimated output is calculated and compared to the measurements. Through observing the innovation of the EKF, the performance of each of the redundant sensors is monitored. The estimated covariance of the innovation is then used to automatically isolate the failing sensors." @default.
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- W1977194837 date "2012-01-01" @default.
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- W1977194837 title "Sensor Fault Detection and Isolation using Adaptive Extended Kalman Filter" @default.
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- W1977194837 doi "https://doi.org/10.3182/20120829-3-mx-2028.00195" @default.
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