a machine learning approach to pitot static error ...€¦ · orion uav, aurora flight sciences...

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

)