Improving drone flight safety using machine IVHM COE learning · Improving drone flight safety...

IVHM COE

http://www.cranfield.ac.uk/ivhmIVHM©2012 Cranfield University All rights ReservedIVHM©2015 Cranfield University All rights Reserved

Improving drone flight safety using machine

learningJonathan Pelham6th October 2015

http://www.cranfield.ac.uk/ivhmIVHM©2015 Cranfield University All rights Reserved

IVHM©2015 Cranfield University All rights Reserved http://www.cranfield.ac.uk/ivhmIVHM©2015 Cranfield University All rights Reserved

“Data does not become information until it has been understood in the mind of the pilot.”

“Data does not become information until it has been understood in the mind of the pilot.”

Introduction


• Production, certification & testing

• Total ownership costs• System & life cycle • Requirements• FMECAs• Design models• Failure modes/models• System test data

Design EngineeringManufacturing

• Maintenance Scheduling• Spares Supply• Asset Tracking• Maintenance Execution

Vehicle Maturation/ New Product

• Operational Demand• Fleet Availability• MR & O leading

• Operational Schedule• Operational Effectiveness

Health Status

Act Acquire

Transfer

Sense

Health Status• Current• Predicted

Analyse

Asset

Data Repository& Ground Processing

Maintenance & Logistics

Operational Control

• Production, certification & testing

• Total ownership costs• System & life cycle • Requirements• FMECAs• Design models• Failure modes/models• System test data

Design EngineeringManufacturing

• Maintenance Scheduling• Spares Supply• Asset Tracking• Maintenance Execution

Vehicle Maturation/ New Product

• Operational Demand• Fleet Availability• MR & O leading

• Operational Schedule• Operational Effectiveness

Health Status

Act Acquire

Transfer

Sense

Health Status• Current• Predicted

Analyse

Asset

Data Repository& Ground Processing

Maintenance & Logistics

Operational Control

Integrated Vehicle Health Management


Flight!


RPAS – a refresher

Operator Tasks• Aviate• Navigate• Communicate• Manage systems

OODA LoopObserve -Gather informationOrient -Put Information in contextDecide-Decide correct actionAct -Perform Action

IVHM©2013 Cranfield University All rights Reserved

UAS Architecture changes with increasing size

6

Single BoardPicopter

Federated Avionics

Global HawkMulti-Board

Demon



USAF RPAS Class AMishap Rate

General Aviation Rate 1.2/100,000 fh

Boeing 737 Rate 0.13/100,000 fh


RPAS Information flow timescales


A BC

D E FG

Flight I

Flight Data Problems

10Hz 16bit ADC ~

1.2Mb per Minute

30+ Channels ~ 36Mb per minute

Flight 7hrs+ ~

2.1Gb per hour ~ 14.4Gb

Multiple aircraft – Different versions

Multiple Tb per day


The Immune system

B-Cells

T-Cells


Feature space

Example 2-dimensional feature space• aircraft velocity• load factor

12

34

CaseData

X


Feature spacewith AIS

AIS LearningSelf/Non-Self discriminationLearning operational rules from flight data

1. Flight mishap cases within feature space

2. Non mishap Self within feature space

3. Simplified rule for aircraft on-board use


Results


• Flight History & maintenance data

• better power & processing

• Local humans• Limited bandwidth

• “Fresh” data• Better bandwidth

• Limited power & processing

RPAS AIS structure discussion


• RPAS need to improve their mishap rate and the lessons learned integrated into future designs

• Machine Learning may be a solution • But not suitable for FMS deployment

• Integrated Vehicle Health management has many opportunities in RPAS

• Linux and Open Source have a major role to play

Conclusions


Any [email protected]

Improving drone flight safety using machine IVHM COE learning · Improving drone flight safety...

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