A Machine Learning Approach to Aircraft Sensor Error Detection and Correction

[+] Author and Article Information
Renee C. Swischuk

178 Marlborough Street Unit BR Boston, MA 02116 swischuk@mit.edu

Douglas Allaire

3123 Texas A&M University College Station, TX 77843 dallaire@tamu.edu

1Corresponding author.

Manuscript received November 14, 2018; final manuscript received April 17, 2019; published online xx xx, xxxx. Assoc. Editor: Ying Liu.

ASME doi:10.1115/1.4043567 History: Received November 14, 2018; Accepted April 17, 2019


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 detecting sensor failures and predicting 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 occuring. Feature selection of redundant sensor data in combination with k-nearest neighbors regression is used to predict 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.

Copyright © 2019 by ASME
Your Session has timed out. Please sign back in to continue.





Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In