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SBIR/STTR

In-Flight and Pre-Flight Detection of Pitot Tube Anomalies, Phase I

Completed Technology Project

Project Introduction

In-Flight and Pre-Flight Detection of Pitot Tube Anomalies, Phase I
The health and integrity of aircraft sensors and instruments play a critical role in aviation safety. However, inaccurate or false readings from these same sensors/instruments can lead to improper decision-making resulting in serious if not fatal consequences. This proposal offers a research and development (R&D) effort to demonstrate the feasibility of using advanced data analysis techniques to identify failures in pitot tubes resulting from blockage, icing, or moisture. These data analysis techniques will use existing electrical signals of pitot tube sensors that result from measured processes during in-flight conditions and/or induced signals in pre-flight conditions to detect anomalies in the sensor readings. The proposed method for detecting pitot tube anomalies is referred to as the "noise analysis" technique. This technique has been validated and is currently and routinely used by the proposing firm and others for detecting sensing line blockages of pressure transmitters in nuclear power generating stations; a very similar issue to the concern associated with pitot tube blockages. Typically, the output of a sensor that is measuring a process (e.g. air flow) contains two components: a static (DC) component that represents the process parameter, and a dynamic (AC) component. Through the use of the dynamic component of existing electrical signals, the dynamic response of the sensor can be measured in the frequency domain. As the sensor becomes blocked or degraded, changes to the dynamic response can be observed. Specific examples of this are given in the proposal. Another consideration in this proposal is diagnosing pitot tube sensor anomalies in pre-flight conditions. In pre-flight checks, the pitot tubes reside in mild conditions and will not be measuring a turbulent process. As such, a technique is proposed to induce this type of noise on the sensor input and analyze the resultant output using the same noise analysis technique. More »

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