a machine learning approach to pitot static error ...€¦ · orion uav, aurora flight sciences...
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A Machine Learning Approach to Pitot Static Error Detection & Airspeed Prediction
Renee Swischuk1, Douglas Allaire2
1Texas A&M University, Department of Mathematics2Texas A&M University, Department of Mechanical Engineering
Objective
Error Detection • Two running estimates of airspeed from pitot tube and
accelerometer data:
𝑉𝑝𝑖𝑡𝑜𝑡 (𝑡) = 2(𝑃𝑡(𝑡)−𝑃𝑠(𝑡))
ρ𝑎𝑖𝑟(𝑡)𝑉𝐼𝑁𝑆 𝑡 = 𝑡−1
𝑡𝑎𝑥 𝑑𝑡
• Anytime |𝑉𝑝𝑖𝑡𝑜𝑡 − 𝑉𝐼𝑁𝑆| > 𝑉𝑡ℎ𝑟𝑒𝑠ℎ, classify the state of the system.
System states:1. Pitot tube block – Airspeed indicator drops to 02. Pitot drain block – Total pressure constant3. Static port block – Static pressure constant
• Hundreds of redundant data streams are recorded in flight.• Sammons Mapping Function3 was minimized to find lower
dimensional data sets which preserve the structure and distances of the original high dimensional data:
E =
𝑖<𝑗𝑛 [𝑑𝑖𝑗
∗ − 𝑑𝑖𝑗]2
𝑑𝑖𝑗∗
𝑖<𝑗𝑛 𝑑𝑖𝑗
∗
Classifying the State of the System • Erroneous pressure streams are manually simulated for 100 seconds.
• Autocorrelation for each pressure stream is stored in the error library.• Autocorrelation is calculated online every 10 seconds.• Online case is classified into the error library to detect constant
signals.• System state is determined using minimum Euclidean distance.
Offline Airspeed Library Results
Airspeed Prediction
Drain
Ram Air
Static Air
Pitot Tube
Pitot Connection
Diaphragm
HandstaffPinion
Long Lever Sector
The Effects of Pitot+Drain Block on Total Pressure
47450
47400
47350
47300
47250
47200
47150
47100
Tota
l Pre
ssu
re (
Pa)
0 50 100 150 200
Airspeed Prediction450
400
350
300
250
200
150
100
50
0 2000 4000 6000 8000 10000
Air
spe
ed
(kn
ots
)
Time (sec)
• Airspeed predicted using K-nearest neighbors regression.
• This system is able to detect error within 20 seconds.• Airspeed prediction is within 30 knots of the true airspeed during
cruise.
• This system will allow pilots to safely navigate an aircraft in the event of a pitot static error and provide UAV’s the ability to continue flight.
Time (sec)
Unmanned Aerial Vehicles only have one pitot static system used for guidance so system failure can result in critical missions being aborted. If autonomous error detection software is developed, these missions may be able to continue on.
Motivation
Our goal is to create a system capable of detecting pitot static blocks within 30 seconds of failure. Failures result in inaccurate airspeed readings so in addition, we attempt to predict a corrected airspeed which lies within 10 knots of the true airspeed.
Approach
1 U.S. Dept. of Transportation, Federal Aviation Administration , Pilot's handbook of aeronautical knowledge, 2008.2King, A. D., B.Sc, F.R.I.N., ”Inertial Navigation - Forty Years of Evolution”, GE Company Review, Vol 13, No 3, 19983LLC, CH ROBOTICS. "Understanding Euler Angles.“ November 26th, 20124Camastra, Francesco. "Data dimensionality estimation methods: a survey." Pattern recognition 36.12 (2003): 2945-2954.
Inertial Navigation System 1
• In the reduced dimension, the following most informative features for predicting airspeed were chosen using simulated annealing optimization:
Body Coordinate System2
Using highly reliable Inertial Navigation System (INS) data, we can detect pitot static errors and trigger a classification model for identifying the state of the system. In the event of a failure, a corrected airspeed is predicted using k-nearest neighbors in an offline/online paradigm. Initial feature selection and reduction is performed on the high dimensional flight data.
References
500400 300200100
0
500
400
300
200
100
0Erro
r (k
no
ts)
Orion UAV, Aurora Flight Sciences
Pitot Static System Diagram1
0 2000 4000 6000 8000 10000
Time (sec)
500400 300200100
0
?
Example of K-Nearest Neighbor Classification K=3
Class ( ) = ?
Choosing K
Pe
rce
nt
Co
rre
ct
.978
.977
.976
.975
.974
.973
.9720 2 4 6 8 10 12 14
• Fuel Flow• Engine Core
Speed• Engine Fan
Speed
• Pitch• Angle of
Attack• Thrust
Target
• Thrust Command
Roll
Pitch
Yaw
Z
Y
XAccelerometers
Gyros
VINS
Vpitot
Vpredicted
Error
Vthresh
Systemstate
This work was partially supported by FA9550-16-1-0108, under the Dynamic Data-Driven Application Systems Program, Program Manager Dr. Frederica Darema.
4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Number of Data Streams (Dimension)
Error Detection and Airspeed Prediction
Dimensionality Reduction.040
.035
.030
.025
.020
.015
.010
.005
0.00
-.005
Ave
rage
Str
uct
ure
Lo
ss
Higher dimensionality has minimal gain
Number of Neighbors (k)
Air
spe
ed
(kn
ots
)