Research Papers

A Machine Learning Approach to Aircraft Sensor Error Detection and Correction

[+] Author and Article Information
Renee Swischuk

Department of Mathematics,
Texas A&M University,
College Station, TX 77843
e-mail: swischuk@mit.edu

Douglas Allaire

Assistant Professor
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 19(4), 041009 (Jun 06, 2019) (12 pages) Paper No: JCISE-18-1301; 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 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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Fig. 1

The offline (upper) and online (lower) portions of our approach

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Fig. 2

Behavior of test functions: (a) g0(x1, x2) versus x1 and x2, (b) g1(x1, x2) versus x1 and x2, and (c) g2(x1, x2) versus x1 and x2

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Fig. 3

Prediction accuracy for three functions using different subsets of the features: (a) g0(x1, x2) versus x1 and x2, (b) g1(x1, x2) versus x1 and x2, and (c) g2(x1, x2) versus x1 and x2

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Fig. 4

Relationship between various sensor outputs during different portions of flight: (a) body longitudinal acceleration versus airspeed during climb, (b) angle of attack versus airspeed during climb, (c) thrust command versus airspeed during cruise, (d) fuel flow versus airspeed during climb, and (e) altitude versus airspeed during approach

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Fig. 5

The effects of holding static pressure (a) and total pressure (b) constant for 100 s during four sections of flight. The solid line denotes the true airspeed, and the dashed line denotes the airspeed computed from the pitot static system (using Eq. (3)). (a) Static port block and (b) pitot tube block.

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Fig. 6

Three general ways a total pressure stream behaves during flight

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Fig. 7

Autocorrelation behavior of three types of data signals

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Fig. 8

Airspeed prediction for a single flight

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Fig. 9

Upper, detection of two pitot tubes (PTBs) and one static port block (SPB); lower, airspeed error. Shaded columns denote actual duration of block.

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Fig. 10

Error produced by the pitot static and predicted airspeeds as a function of altitude for a pitot tube block



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