Sensors are crucial to modern mechanical systems. The location of these sensors can often make them vulnerable to outside interferences and failures, and the use of sensors over a lifetime can cause degradation and lead to failure. If a system has access to redundant sensor output, it can be trained to autonomously recognize errors in faulty sensors and learn to correct them. In this work, we develop a novel data-driven approach to detect sensor failures and predict the corrected sensor data using machine learning methods in an offline/online paradigm. Autocorrelation is shown to provide a global feature of failure data capable of accurately classifying the state of a sensor to determine if a failure is occurring. Feature selection of the redundant sensor data in combination with k-nearest neighbors regression is used to predict the corrected sensor data rapidly, while the system is operational. We demonstrate our methodology on flight data from a four-engine commercial jet that contains failures in the pitot static system resulting in inaccurate airspeed measurements.
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December 2019
Research-Article
A Machine Learning Approach to Aircraft Sensor Error Detection and Correction
Douglas Allaire
Douglas Allaire
Assistant Professor
Department of Mechanical Engineering,
College Station, TX 77843
e-mail: dallaire@tamu.edu
Department of Mechanical Engineering,
Texas A&M University
,College Station, TX 77843
e-mail: dallaire@tamu.edu
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Renee Swischuk
Douglas Allaire
Assistant Professor
Department of Mechanical Engineering,
College Station, TX 77843
e-mail: dallaire@tamu.edu
Department of Mechanical Engineering,
Texas A&M University
,College Station, TX 77843
e-mail: dallaire@tamu.edu
1Corresponding author.
Manuscript received November 14, 2018; final manuscript received April 17, 2019; published online June 6, 2019. Assoc. Editor: Ying Liu.
J. Comput. Inf. Sci. Eng. Dec 2019, 19(4): 041009 (12 pages)
Published Online: June 6, 2019
Article history
Received:
November 14, 2018
Revision Received:
April 17, 2019
Accepted:
April 17, 2019
Citation
Swischuk, R., and Allaire, D. (June 6, 2019). "A Machine Learning Approach to Aircraft Sensor Error Detection and Correction." ASME. J. Comput. Inf. Sci. Eng. December 2019; 19(4): 041009. https://doi.org/10.1115/1.4043567
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