AOPA Pilot Magazine - August 2021 - 98

RUDDER & WRENCH
Cylinders with failing exhaust valves don't always exhibit these
telltale EGT oscillations. If the valve's rotator cap stops working
properly or if the valve starts sticking in its guide because of
deposit buildup, the valve might start heating unevenly and burning
badly, but the EGT will not oscillate. FEVA 1.0 couldn't detect
such failures, and our human analysts might not spot them, either.
It became apparent that any algorithm capable of reliability
predicting incipient exhaust valve failures might need to look for
more than just EGT oscillations and might need to look at more
than just EGT. It might need to look for complex patterns and
interrelationships among data obtained from many different sensors.
What sensor data should it be looking at? What patterns and
interrelationships should it be looking for? We had no clue. Nor,
for that matter, did Continental, Lycoming, Rotax, or anyone else
in the industry.
" I bet we could get the computer to figure this out! " exclaimed
my colleague Chris Wrather to me over brunch one day. Chris
has a unique background-entrepreneur, airplane owner, instrument
pilot, A&P mechanic, computer programmer, and Ph.D.-level
of input variables than humans are. Also, machine learning models
keep getting smarter over time as they get fed more training data.
Chris became convinced that a FEVA 2.0 based on machine
learning was a better way to go. His plan was to program the computer
with a generalized machine-learning algorithm known as
the " random forests " model whose inputs would include pretty
much everything we could get from the aircraft's digital engine
monitor-more than 30 different input variables.
The model would then be " trained " by feeding it data from
thousands of actual GA flights, some involving engines known to
have failing exhaust valves, others from engines with valves known
to be healthy. For each flight in this " training set " we would tell
the model whether the flight was from a healthy-valve engine or
a sick-valve engine and which cylinder(s) had failing valve(s). It
would then be up to the machine learning model to figure out how
to tell the difference.
After much training and back-testing, we deployed FEVA 2.0
about a year ago. It proved superior to FEVA 1.0 in every respect. It
caught more failing exhaust valves. It generated fewer false alarms.
The slow, rhythmic oscillation of EGT #5 suggests that the exhaust valve is failing.
mathematician. Chris had just completed an online course in
machine learning offered by Stanford University.
A better mousetrap
Suppose you wanted to create a computer algorithm that could
analyze a digital photo and determine whether it was of a man
or a woman. One way of doing that would be to assemble a group
of experts and ask them to come up with a set of rules-such as
men usually have broader shoulders and more facial hair, women
have more curves and longer hair-and programming those rules
into the computer. Another way would be to feed the computer
thousands of photos, each one identified as male or female, and
let the computer figure out for itself how to tell which is which.
The former is called an " expert system " while the latter is called
" machine learning. "
Machine learning has some distinct advantages over the expert
system approach. Computers are generally a lot better at figuring
out complex patterns and interrelationships among a large number
98
AOPA PILOT / August 2021
In contrast to FEVA 1.0's simple go/no-go output, the FEVA 2.0
model yielded a numeric " risk score " for each cylinder that could
range anywhere from very low risk to very high risk. Any time
the model produced an above-average risk score for one or more
cylinders, we would advise the aircraft owner to have those cylinders
borescoped ASAP.
In May, we released FEVA 2.1, a significantly improved version
of the machine learning. This improved version differs from
its predecessor in two important ways:
* It extends the list of input variables to more than 35; and
* It draws its input data from an aircraft's entire recent flight
history, rather than from only the most recent flight as was
previously the case.
The new model is both 20 percent better at predicting failing
valves ( " sensitivity " ) and 20 percent less likely to make a
false prediction ( " positive predictive value " ) than its predecessor.
In addition, successive reports will be more consistent
over time.

AOPA Pilot Magazine - August 2021

Table of Contents for the Digital Edition of AOPA Pilot Magazine - August 2021

Contents
AOPA Pilot Magazine - August 2021 - Intro
AOPA Pilot Magazine - August 2021 - BB1
AOPA Pilot Magazine - August 2021 - BB2
AOPA Pilot Magazine - August 2021 - Cover1
AOPA Pilot Magazine - August 2021 - Cover2
AOPA Pilot Magazine - August 2021 - Contents
AOPA Pilot Magazine - August 2021 - 2
AOPA Pilot Magazine - August 2021 - 3
AOPA Pilot Magazine - August 2021 - 4
AOPA Pilot Magazine - August 2021 - 5
AOPA Pilot Magazine - August 2021 - 6
AOPA Pilot Magazine - August 2021 - 7
AOPA Pilot Magazine - August 2021 - 8
AOPA Pilot Magazine - August 2021 - 9
AOPA Pilot Magazine - August 2021 - 10
AOPA Pilot Magazine - August 2021 - 11
AOPA Pilot Magazine - August 2021 - 12
AOPA Pilot Magazine - August 2021 - 13
AOPA Pilot Magazine - August 2021 - 14
AOPA Pilot Magazine - August 2021 - 15
AOPA Pilot Magazine - August 2021 - 16
AOPA Pilot Magazine - August 2021 - 17
AOPA Pilot Magazine - August 2021 - 18
AOPA Pilot Magazine - August 2021 - 19
AOPA Pilot Magazine - August 2021 - 20
AOPA Pilot Magazine - August 2021 - 21
AOPA Pilot Magazine - August 2021 - 22
AOPA Pilot Magazine - August 2021 - 23
AOPA Pilot Magazine - August 2021 - 24
AOPA Pilot Magazine - August 2021 - 25
AOPA Pilot Magazine - August 2021 - 26
AOPA Pilot Magazine - August 2021 - 27
AOPA Pilot Magazine - August 2021 - 28
AOPA Pilot Magazine - August 2021 - 29
AOPA Pilot Magazine - August 2021 - 30
AOPA Pilot Magazine - August 2021 - 31
AOPA Pilot Magazine - August 2021 - 32
AOPA Pilot Magazine - August 2021 - 33
AOPA Pilot Magazine - August 2021 - 34
AOPA Pilot Magazine - August 2021 - 35
AOPA Pilot Magazine - August 2021 - 36
AOPA Pilot Magazine - August 2021 - 37
AOPA Pilot Magazine - August 2021 - 38
AOPA Pilot Magazine - August 2021 - 39
AOPA Pilot Magazine - August 2021 - 40
AOPA Pilot Magazine - August 2021 - 41
AOPA Pilot Magazine - August 2021 - 42
AOPA Pilot Magazine - August 2021 - 43
AOPA Pilot Magazine - August 2021 - 44
AOPA Pilot Magazine - August 2021 - 45
AOPA Pilot Magazine - August 2021 - 46
AOPA Pilot Magazine - August 2021 - 47
AOPA Pilot Magazine - August 2021 - 48
AOPA Pilot Magazine - August 2021 - 49
AOPA Pilot Magazine - August 2021 - 50
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AOPA Pilot Magazine - August 2021 - 53
AOPA Pilot Magazine - August 2021 - 54
AOPA Pilot Magazine - August 2021 - 55
AOPA Pilot Magazine - August 2021 - 56
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AOPA Pilot Magazine - August 2021 - 62
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AOPA Pilot Magazine - August 2021 - 65
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AOPA Pilot Magazine - August 2021 - 96
AOPA Pilot Magazine - August 2021 - 97
AOPA Pilot Magazine - August 2021 - 98
AOPA Pilot Magazine - August 2021 - 99
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AOPA Pilot Magazine - August 2021 - 109
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AOPA Pilot Magazine - August 2021 - 112
AOPA Pilot Magazine - August 2021 - Cover3
AOPA Pilot Magazine - August 2021 - Cover4
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