The health and integrity of aircraft sensors and instruments play a critical role in aviation safety. Inaccurate or false readings due to icing of airspeed sensors in flight can lead to improper decision-making, resulting in serious consequences. Icing or blockages of pitot airspeed sensors provide very little indication of sensor malfunction. Sensor output may indicate high, low, or nonfunctioning state, and not be responsive to actual changes in airspeed.
This innovation provides an assessment of the health and reliability of pitot airspeed signals in flight, as well as a capability for testing the pitot sensor pre-flight. The output of a process sensor contains a static component representing the process parameter and a dynamic component representing the process fluctuations. Using the dynamic component, the dynamic response of the sensing system can be determined.
As the sensor is impaired or degraded, changes to the dynamic response are observed. Various algorithms, including autoregressive modeling and statistical evaluation of the pitot airspeed signal, were integrated into a pitot health monitoring system enabling identification of icing events that degraded the pitot airspeed signal output. The realtime evaluation of the dynamic content of the process sensor’s signal output is used to assess the health and reliability of the sensing channel.
As all aircraft rely on the accurate and reliable performance of pitot/static systems, improving the detection of inaccurate indications would increase safety to passengers and crew, reduce the potential for accidents, and lead to other advances in aviation technology.
This work was done by Hashem Hashemian of Analysis & Measurement Services Corp. for Glenn Research Center.