Abstract
This paper presents a Fault Detection and Isolation (FDI) method for Air Data Sensors (ADS) of aircraft. In the most general case, fault detection of these sensors on modern aircraft is performed by a logic that selects one of, or combines 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 saving of the aircraft because there is no necessity anymore to increase the number of sensors. Furthermore, the analytical redundancy can contribute to the structural design optimization. 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 a metric derived from the innovation of the Extended Kalman Filter (EKF), the performance of each of the redundant sensors is monitored. This metric is then used to automatically isolate the failing sensors.
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van Eykeren, L., Chu, Q. (2013). Air Data Sensor Fault Detection Using Kinematic Relations. In: Chu, Q., Mulder, B., Choukroun, D., van Kampen, EJ., de Visser, C., Looye, G. (eds) Advances in Aerospace Guidance, Navigation and Control. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38253-6_12
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DOI: https://doi.org/10.1007/978-3-642-38253-6_12
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