2017 Heli-Expo - Helicopter FDM Research.

Federal Aviation
Administration
Helicopter Flight
Data Monitoring
(HFDM) for
ASIAS
Presented to:
Rotor Safety Challenge
Session @ HeliExpo. 2017
Presented By:
Cliff Johnson, Research Program
Manager/Engineer, FAA William J. Hughes
Technical Center, Atlantic City, NJ
Kyle Collins, Ph.D. Principal Investigator,
Georgia Technical University, Atlanta, GA
Keith M Cianfrani, LTC (ret), MAS. RSP,
Helicopter Association International
(HAI)/Florida Institute of Technology (FIT)
Mar. 8, 2017

2Federal Aviation AdministrationAviation Research Division, ANG-E FOR OFFICIAL USE ONLY
Briefing Outline
• Introduction and Overview – ASIAS & HFDM Research
• HFDM Research Team/Partners
• Update on Rotorcraft HFDM Research
– Defining Safety Metrics for Rotorcraft Operations
– HFDM with Data-Enhanced Helicopter Performance Models
– Cockpit Video Data Analysis For Rotorcraft Safety
– Phase of flight and Anomaly detection with Data Mining
– Helicopter Airborne Data Recording and Analysis System (in development)
– HFDM Flight Testing
• Participation in HFDM Research for ASIAS/How it Works/Outreach/Benefits
• Questions?

ASIAS – Aviation Safety Information
Analysis and Sharing
• Q: What is ASIAS?
• A: The Aviation Safety Information Analysis
and Sharing (ASIAS) program is a collaborative
government and industry initiative to share and
analyze data to proactively discover system
safety concerns before accidents or incidents
occur, leading to timely mitigation and
prevention.

HFDM Research for ASIAS
What are we doing?
Rotorcraft research is underway to develop more robust helicopter
data and new analytical tools designed for the unique nature of
helicopter operations. HFDM research lays the foundation for
future helicopter data analysis in ASIAS and supports the USHST’s
efforts to reduce the helicopter fatal accident rate. This research will:
• Collect flight data from commercial, government, flight training,
and other large and small operators from various mission
segments to be used for analysis;
• Explore flight data monitoring as a voluntary means to improve
safety across the industry;
• Develop secure, confidential, and protected safety analysis of
aggregate flight data records;
• Support risk mitigation efforts through the ASIAS program.

HFDM Research for ASIAS
What are we not doing?
Rotorcraft HFDM Research for ASIAS & the ASIAS
Program is not:
• A replacement for an operator’s HFOQA/HFDM
Program or HFOQA/HFDM Vendor’s Service
• An FAA certification program
• An FAA enforcement program
• Mandatory (all participation is voluntary)

Why Rotorcraft FDM Research
Supporting ASIAS?
2-Mar-17

U.S. Helicopter Fatal Accident Rate
Flight hours source:
-5 Yr Baseline from FAA's GA & Part 135 Activity
Survey.
-2016 from FAA's Aerospace Forecast FY2016-2036.
Goal by end of CY 2019:
20% reduction from 5 year
baseline

2017 NTSB Most Wanted List –
Expand Recorder Use to Enhance Safety

But what’s in a Flight Data Recorder/Flight
Data Monitoring System?
• Helicopter Flight Data Recorders/Helicopter Flight Data
Monitoring Devices typically consist of one or more of the
following items:
– Flight Data Recorder
– Audio/Video Sensors
– Attitude and Heading Reference System (AHRS)
– Accelerometers/Gyroscopes
– GPS Inputs
– Avionics (Flight Management System, Air Data Computer, etc.)
Inputs (usually ARINC-429 or ARINC-717)
– Engine/Rotor Inputs/Sensors

10Federal Aviation
Administration
WAAS Alaska WRS Telco Analysis
Helicopter Flight Data Recorder (FDR) –
Standalone device or interfaced with on-board
sensors
Power supply
(aux GPS
signal)
Power
supply
http://www.aircraftspruce.com/catalog/avpages/garminAntenna.php
https://www.appareo.com/aviation/flight-data-monitoring/
ARINC 717/other
standard
FDAU
Microphones
Cockpit camera
North FDS QAR
Engine
monitoring
(Intercom
audio)
GPS Antenna
Satellite
Antenna
GPS/AHRS
http://www.expaircraft.com/freeflight.htm
http://www.northfds.com/products.html
Vision 1000
Tablet

11Federal Aviation
Administration
Installation Locations - FDR
Safetyplane V4 (Helicom V1) recorder in
cockpit or remotely located
http://easa.europa.eu/system/files/dfu/Final_Report_EASA.2008-7.pdf
https://www.appareo.com/aviation/flight-data-monitoring/vision-1000/
Appareo Vision
1000 mounted in
AS350 cockpit

12Federal Aviation
Administration
Installation Locations – Visual
Display
http://enstromhelicopter.com/2013/03/enstrom-selects-garmin-g1000h-
integrated-flight-deck-for-480b/
http://www.verticalmag.com/features/features_article/DigitallyEnhanced/
http://www.sportys.com/pilotshop/flight-gear-ipad-mini-kneeboard.html

Installation Locations – GPS Antenna,
AHRS
http://www.robinsonheli.com/manuals/r22_mm/r22_mm_14.pdf
http://www.grounddatasolutions.com/airbornelaser.html
http://www.arrowaviationco.com/site321.php
Bell 212Bell 412
http://shop.avionics.co.nz/lcr100-stc-bell&view=mobile#container
GPS Antenna
AHRS
GPS Antenna
AHRS
GPS Antenna

Rotorcraft FDM Research
Supporting ASIAS

FAA Helicopter
Research Team Members
Prof. Dimitri Mavris – PI
Kyle Collins, Ph.D. – Co-PI
Simon Briceno
Alexia Payan
Hsiang-Jui Chin
Po-Nien Lin
Prof. Karen Marais – PI
Inseok Hwang
Sanghuyn Shin
Prof. Stephen Cusick – PI
Keith Cianfrani
Cliff Johnson – Rotorcraft FDM
Research Task Lead
Lacey Thompson – Ops Research
Analyst
FAA WJHTC Atlantic City, NJ
Alex Alshtein – ASIAS Group Leader
Nicky Armour –
MITRE-CASSD
*Note: MITRE role involves collaboration with research
activities for integration with ASIAS.
Ed DiCampli, Chief Operating Officer
Robert Liguori, IT Consultant
Keith Cianfrani, FDM Specialist/Outreach
Tyler Travis
Lana Manovych

Helicopter Flight Data Monitoring
Research Partners
Entities Involved:
• USHST
• IHST
• Helicopter Flight Data Monitoring
Manufacturers
• Helicopter Original Equipment
(OEM) Manufacturers
• Helicopter Operators
• Many more, too many to list…
• We highly value, encourage, and
need industry participation in
this effort in order to make it a
success!!!

Rotorcraft FDM Research
Analysis
Capabilities
Supports the USHST Goal of 20% Reduction in
the Fatal Accident Rate for Helicopters by 2020
 Safety Metrics
 HFDM Analysis
Techniques
 HFDM Modeling
Techniques
 HFDM Cockpit
Audio/Video Analysis
 Data Mining for Safety
Events
 Identification of High-Risk
Safety Events
 Industry Involvement
 Potential Mitigation Partner
 FDM Equipment Working
Group
 Program
Management
 Outreach
 Flight Tests
 HFDM Device
Integration &
Calibration
 Data Analysis
 Modeling &
Simulation
 Outreach
 Data Transcription
 HFDM Research Repository
 HFDM Architecture Design
 HFDM Working Group

DEFINING SAFETY METRICS FOR
ROTORCRAFT OPERATIONS

Safety Metrics for Rotorcraft Operations
• Tackled so far:
– Proximity to obstacles
– Proximity to weather
– Autorotation
– Tip over taxi (roll-over)
– Vortex Ring State (VRS)
• In progress:
– Unstabilized approach
– Helipad overrun
– Loss of Tail Rotor
Effectiveness (LTE)
High
Priority
In flight
LOC
Autorotation
Unstabilized
approach
System
failure/malfunctionCFIT
Helipad overrun
Proximity to
Obstacles/Weather/Other aircraft
On the
ground
Roll-over
Medium
priority
In flight LOC
LTE
IMC
Fuel low
Close to
ground
Abnormal runway
contact
Vortex Ring State
Unstabilized
approach
Hard landing
Low
priority
In flight
Bird strike
External load
On the
ground
Runway
incursion/excursion
Abrupt maneuver

Proximity to Obstacles Safety Metric
FAA Digital Obstacle File (DOF) +
Flight Data Records
Speed vector
Closure speed
Closure Distance
N
E
ϕ
(lat,long)
(lat,long)
Kinematics
Visualization (Multi-Flights)
-84.55 -84.5 -84.45 -84.4 -84.35 -84.3 -84.25
33.76
33.78
33.8
33.82
33.84
33.86
33.88
33.9
33.92
Flight Paths and Location of Time Critical Obstacles
Recording 1
Recording 2
Recording 3
Recording 4
Recording 5
Recording 6
Recording 7
Longitude
Latitude
0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
0
0.5
1
1.5
2
2.5
3
3.5
4
Distribution of Closure Times for Time Critical Obstacles Within the Safety Box
Closure Time
Counts
Display obstacles located within a specified
“closure time” from the helicopter (ex: 5 s)
Location of “time critical” obstacles
Dist. of closure times for “time critical” obst.
Contour lines are closure times
(0 to 30 seconds)
Latitude: 33.85
Longitude: -84.36
Type: Building
Quantity: 1
Height AGL: 355 ft
Lighting: Flood
Marking: None
Closure Time: 4.9 s
ClosureSpeed(kt) Distance to Obstacle (s)

W th it l l
1 2 3 4 5 6
0
10
20
30
40
50
60
Median, 25th and 75th percentiles of percentage of flight time
spent at each weather severity level for all the flights considered
(outliers are shown as red crosses)
%ofRecordingTime
Weather Severity Level
Weather severity level
0 1 2 3 4 5 6 7
0
10
20
30
40
50
60
%ofRecordingTime
Weather Severity Level
More than ~10 flights
CIWS Weather Severity Levels +
Flight Data Records
Vertical Integrated Liquid water (VIL) 1 2 3 4 5 6
0
10
20
30
40
50
60
Percent of flight time for flight spent at each weather severity level
Recording 1
Recording 2
Recording 3
Recording 4
Recording 5
Recording 6
Recording 7
Recording 8
Weather Severity
Level
%ofRecordingTime
-84.55 -84.5 -84.45 -84.4 -84.35 -84.3 -84.25
33.76
33.78
33.8
33.82
33.84
33.86
33.88
33.9
33.92
Recording 1
Recording 2
Recording 3
Recording 4
Recording 5
Recording 6
Recording 7
Recording 8
Longitude
Latitude
Less than ~10 flights
Proximity to Weather Safety Metric
-84.55 -84.5 -84.45 -84.4 -84.35 -84.3 -84.25
33.76
33.78
33.8
33.82
33.84
33.86
33.88
33.9
33.92 Flight #8
Longitude
Latitude
Black – Wx Sev. 1
Blue – Wx Sev. 2
Green – Wx Sev. 3
Yellow – Wx Sev. 4
Magenta – Wx Sev. 5
Red – Wx Sev. 6

Unstabilized Approach Safety Metric
Dist. of Altitude Deviation Indicator
Altitude Deviation Indicator
Frequency
Approach Angle
Altitude
Vertical Speed
50% of total
path distance
Average location of changes in approach
parameters
Frequency
Start
Ideal profile
Deviation from Ideal Altitude Profile
Altitude(ft)
Path Distance (mile)
Altitude(ft)
Latitude (deg.) Longitude (deg.)
Approach phases identification
Altitude(ft)
Time (min)
Clustering of Approaches by Stability Levels Using Data Mining (k-mean)
Approach Angle Deviation Indicator
AltitudeDeviationIndicator
Identified Approaches
Alt. Profile of Flight Data
(Most Stable)
(Rather sable)
(Less stable)
(Mostly un stable)

Risk of Helipad Overrun (Energy-Based)
Safety Metric
Distance (nm)
0 0.2 0.4 0.6 0.8 1 1.2 1.4
AltitudeMSL(ft)
0
100
200
300
400
500
Altitude Variation for all Approaches
Steep Approach Normal Approach Shallow Approach
Potential Energy
KineticEnergy
( )
ghε
VVε
p
vgk
=
+= 22
2
1
Helicopter
Performance
Specific kinetic energy (ft2/s2)
0 2000 4000 6000 8000 10000
Specificpotentialenergy(ft2/s2)
0
1000
2000
3000
4000
5000
Specific Ec versus Specific EpEk
Vg = groundspeed
Vv = vertical speed
SAFE

HFDM WITH DATA-ENHANCED
HELICOPTER PERFORMANCE
MODELS

Prevalence of Loss of Control Events
25
https://www.faa.gov/regulations_policies/handbooks_manuals/aviation/helicopter_flying_handbook/media/hfh_ch11.pdf
https://www.faasafety.gov/gslac/ALC/course_content.aspx?cID=104&sID=452&preview=true
• More than 30% of
all accidents involve
Loss of Control
(LOC)
• Targeting main LOC
categories
Autorotation VRS Dynamic Rollover

Improved Fidelity of Helicopter
Performance Models
Power available
Autorotation possible at these forward
velocities, at the specified descent rate
-Powerrequired+
ΔP
Power required
Increasedescentrate
Autorotation Vortex Ring State Dynamic Roll Over

Vz/vh
Vx/vh
Prototype HFDM Capabilities to
Recognize Each Undesired State
Autorotation Vortex Ring State Dynamic Roll Over
Vc < 1,000 fpm
Speed, GPS (kt)
Verticalspeed,GPS(kt/min)
Speed, GPS (kt)
Verticalspeed,GPS(kt/min)
Tail rotor pitch (deg)
Lateralcyclic(deg)
Tail rotor pitch (deg)
Lateralcyclic(deg)
Maximumrollangle(deg)
Y (m)X (m)
Z(m)
P,Q < -5% (ok)
-5% < P,Q < 0 (low)
P,Q > 0 (high)
Verticalspeed(ft/min)
Horizontal speed (kt)
Timestamp
Altitude(ftMSL)
Horizontal speed (kt)
Vc < 1,000 fpm
V < 40 kt (low)
V < 35 kt (mid)
V < 30 kt (high)
False alerts
Vertical descent
speed threshold
Estimated
autorotation
boundary
Decreasing
event severity

How? – Improved Fidelity of Helicopter
Performance Models
Autorotation VRS Dynamic Rollover
Actuator disk
Power available
Power required curve
Autorotation possible at these forward
velocities, at the specified descent rate
-Powerrequired+
ΔP
Power required
Increasedescentrate

Result – Prototype HFDM Capabilities to
Recognize Each Undesired State
Dynamic Rollover
VRS
Autorotation
Dynamic Rollover
Vortex Ring StateAutorotation

Future Work
• Enhance existing models
• Tackle other high priority safety
metrics/performance models:
– Proximity to Traffic
– Loss of Tail Rotor Effectiveness (LTE)
– Mast Bumping
– Autorotation Recovery (i.e. Vuichard Technique)
• Integrate HFDM data with Helicopter
Simulators for event playback/training

PHASE OF FLIGHT AND
ANOMALY DETECTION WITH
DATA MINING

Data mining – Phase of Flight Identification
• Divide the flight data records into segments which reflect the
phases of flight for rotorcraft
• Only 3 parameters were used for retrieving the phases
• Filtering thresholds were based on literature / SME survey
• Create general criteria for evaluation of the results
• Phases of flight can be used to understand the characteristics of
flight operations and also assist on anomaly detection
Visualization of thresholds
AltitudeAGL(ft)
Red: Standing
Green: Surface Taxi
Blue: Hover Taxi
Purple: Air Taxi
Yellow: Hover
Khaki: Hover climb
Orange: Hover descent
Piecewise linear
representation
AltitudeAGL(ft) Time (s)
Sample result of high altitude phases

COCKPIT AUDIO/VIDEO DATA
ANALYSIS FOR ROTORCRAFT
SAFETY

Video
Image
Background
Instrument
Panel
Pilots
Audio
Conversation
Alarms
Background
Noise
HFDM Cockpit Video Data Analysis
• Video data can be recorded using
inexpensive equipment and captures
flight information which some Flight
Data Recorders (FDR) do not record
• Supplementary tool that may be used
as a cross-check for verifying
information captured on a Flight Data
Recorder
• Efficient and accurate analysis of
helicopter attitude can be achieved
with video data analysis tools
• Video analysis of helicopter state and
flight parameters could mitigate
potential accidents and further
enhance operational safety

Attitude Estimation using Data
Mining
Flight Information Extraction from
Instrument Panel
HFDM Cockpit Video Data
Processing/Analysis

Audio Data Analysis –
Cockpit Alarm Detection and Identification
• The research team has developed a cockpit alarm detection and
identification algorithm composed of:
– Alarm detection: Short Time Fourier Transform (STFT) to obtain time-
based frequency information and Cumulative Sum Chart (CUSUM) for
statistical change detection
– Alarm identification: Correlation analysis with alarm database
Input:
audio data
Output:
Alarm
types and
occurrence
times
autopilot_disco
dash_MIDDLE

Audio Data Analysis – Engine/Rotor Noise
• Cockpit audio is analyzed in the frequency domain, correlated with
flight parameters and represented as a statistical model
• The noise profile can be used to estimate engine data (such as
torque and/or rpm)
• If flight data is available, it can be compared with estimates to
identify anomalous behavior
Time (seconds)
1020 1040 1060 1080 1100 1120 1140 1160
%Torque
0
50
100
Flight parameters can be estimated by frequency analysis and the
statistical model
% Torque
20 30 40 50 60 70 80 90 100
Frequency(kHz)
16
18
20
22
24
26
28
30
32
1
2
Expected Noise
Engine has a higher than expected frequency for some
data points

HADRAS (Helicopter Airborne Data
Recording and Analysis System)
(Note: Proposed Name)
HADRAS

What is HADRAS?
• MITRE team is working with FAA on
initial HADRAS development
• General Aviation Airborne Recording Device
(GAARD) for Rotorcraft
• Mobile HFDM Device
• Interfaces with portable AHRS units and ADS-B
In devices
• Free Download from App Store on IOS devices
(when completed, exploring Android version as
well)

‘Start Flight’ Screen Updates
User can select
Airport/Heliport
from the list:
o Provides a combo box
that lists selectable
location IDs of nearby
heliports

‘Start Flight’ Screen Updates
User can select Mission Segment
from the list:
o Aerial Applications
o Aerial Photography & Filming
o Air Tour, Airborne Law Enforcement
o Corporate & Business Charter Ops
o Electronic News Gathering
o Environmental Survey, External Loads &
Heavy Lift
o Fire Control/Support
o Flight Training
o General Helicopter Ops (Not Mission Specific)
o Helicopter Air Ambulance
o Heli-Skiing, Offshore Ops
o Oil and Gas Support
o PEGASAS Research
o Personal Use
o Pipeline & Power Lines (HCC)
o Unmanned Aerial Systems
o Wildlife Management & Mustering

‘Active Recording Screen’
Updates
Display Current
Phase of flight:
• Taxi
• Takeoff
• Initial Climb
• Climb
• Cruise
• Descent
• Initial approach
• Final approach
• Landing
• Cruise, etc.

‘Flight Summary Screen’ Updates
Add ability to edit
departures/arrivals
heliports:
o Provide a combo box that lists
selectable location IDs of
nearby heliports
Add ‘Follow Flight Plan’ check
mark

FAA HFDM Test Flights

FAA R&D Test Flights & Data Analysis
• Test Platform
– FAA’s Sikorsky S-76A Helicopter,
Equipped with ADS-B Out (1090ES)
– Equipped with 8+ representative
HFDM/HFDR Devices from industry today
• Research Goals
– Identify, examine, and install several
different HFDM/HFDR units
– Collect/process data from each
HFDM/HFDR system for different
scenarios/conditions
• Research Outputs
– Helicopter FDM “Truth” Data to be used to
define and validate events, parameters,
exceedances, recording rates, etc. from
anomalous data
– Installation guidelines and optimal locations
for each system

ROTORCRAFT FDM Repository –
How It Works & Participation
Data Flow Diagram

Concept of Rotorcraft HFDM Research
Repository for ASIAS
Participating Operators
Analysis
ToolSuite
Creator
Own-Access User
Aggregate
Results
De-identified Data Access User
Program Coordinator
Database Analysis Toolkit Interface & Display Analysis
Tools
All Missions
Flight Training
Air Tour
HAA
OGP
SAR
Heavy Lift
Aerial Application
Logging
Law Enforcement
News Gathering
…
Visualizations,
Algorithms,
Event
Definitions
Secure
System
System Developer
& Administrator
Data Transcriber

HFDM Data Processing Architecture

Helicopter Flight Data Analysis
Conceptual View
FDAU
Microphones
Cockpit camera
QAR
Data acquisition
Data processing
FDR processing computer
Data analysis

HFDM Repository
System Security
• FISMA – Federal Information Security Management Act
Research will follow guidelines, standards and best
practices as outlined by FISMA
• Access – Understand how data access is
granted, who it is granted to and what they
have access to
• Encryption – End to end encryption of all data
during communication as well as encryption
of sensitive “data at rest”
• Integrity – Measure and validate data integrity

Rotorcraft HFDM
MOU
 Memorandum of Understanding (MOU)
• Legal document between Operator and HAI
• States what HAI will be responsible for and what Operator
will be responsible for
• Tailorable to each operator
• All information provided is protected from FOIA
• HFDM data de-identified at operator’s facility, point of when
received at HAI
* Separate MOU for FDM equipment usage

Data Access via MOU’s
• Voluntary sharing of information (only within the
research team via signed non-disclosure agreements)
for research purposes
• Operators sign agreements with HAI, HAI has signed
agreements with PEGASAS (university community)
• HFDM data secured and protected from unauthorized
disclosure including de-identification of data
Governance Framework

Benefits to Participation
• HFDM research can serve as a central conduit for the
exchange of aviation safety information and
analytical capabilities across the community
– Provides insight into emerging risks that may not have been
detected through the assessment of an individual data
sources
– Data is shared and aggregated among participants to more
clearly see precursors to accidents, increasing its potential
value for analysis-based insight
– Implementation of Safety Enhancements will reduce the
likelihood of possible accidents

Benefits to Participation
• Opportunity to participate in data collection activities that may lead to safety
enhancements
• Opportunity to participate in industry information sharing activities
• Opportunity to use aggregate data to identify systemic risks (beyond internal
reporting)
• Similar to the CAST model, a collaboration of industry and government experts
provide input on directed studies to solve issues in the NAS. Rotorcraft specific
areas could include:
– Operations around oil rigs
– Electronic news gathering ops
– Helicopter Air Ambulance ops
– Tour operators (traffic conflicts)
• Knowledge of “what you don’t know” (i.e. hidden risks/dangers) only visible via
sharing of information among parties
• Ability to promote increased situational awareness and safety within helicopter
operations
• Incorporate new safety analysis tools into helicopter operations

How To Participate
• Cliff Johnson - FAA
– Email: charles.c.johnson@faa.gov
– Phone: (609)-485-6181
• LTC (ret) Keith M Cianfrani (HAI/FIT)
– Email: keith.ctr.cianfrani@rotor.com
– Phone: 267-377-5364
• Website: http://hfdm-asias.rotor.com
For More Information, or to Discuss Participation,
Please Contact:

Rotorcraft FDM Timeline
Secure Operator Participation
Develop governance, establish agreements, insure
data protection and confidentiality, test and monitor
data transfer, elicit operator feedback, ensure and
monitor value added to operators, enhance system to
meet emerging operator needs
Develop Capabilities
Requirements analysis, system architecting and
design, implementation, standards for data
formatting/processing prototyping, testing, incremental
delivery of tools and capabilities, integration with
existing communities
Support Rotorcraft Research
Establish generic event set, identify event set gaps,
video/audio processing, safety metrics, software
capabilities, data fusion, accident mapping,
performance models, FDM flight testing, data mining
and knowledge discovery with FDM data
Conduct Outreach and Community Engagement
Establish outreach efforts within the Helicopter
Community, present research topics & results at Heli-
Expo, industry forums/events, HFDM Working Groups,
mitigation partner
2013 2015 2017 2019
Research Prototype || Full Integration

Questions?

2017 Heli-Expo - Helicopter FDM Research.

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