A Machine Learning Approach to Aircraft Sensor Error Detection and Correction

被引:12
作者
Swischuk, Renee [1 ]
Allaire, Douglas [2 ]
机构
[1] Texas A&M Univ, Dept Math, College Stn, TX 77843 USA
[2] Texas A&M Univ, Dept Mech Engn, College Stn, TX 77843 USA
关键词
FAILURE-DETECTION; FAULT-DETECTION; AIRSPEED ESTIMATION; IDENTIFICATION; CAPABILITY;
D O I
10.1115/1.4043567
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
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.
引用
收藏
页数:12
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