Aircraft Maintenance Technology - 31

oday, as airlines face stiff competition and travelers become more
cost conscious, increased operational challenges are placed on
airlines to constantly seek ways of
cutting costs. One area that offers
great potential for doing so is maintenance. Even with recent improvements in
efficiencies, it is estimated that more than 25
percent of maintenance spending is due to
unplanned maintenance, which also drives five
percent of additional wasted fuel consumption. A combination of predictive maintenance
and data analytics promises to yield significant benefits for addressing these problems.
However, there are hurdles that must be overcome to implement these solutions.
Predictive maintenance uses data that is generated
by each aircraft, in combination with operational data,
to determine the health of the systems onboard the
aircraft. Sensors on the aircraft are used to monitor
key parameters, such as air pressure, temperature,
airspeed and fuel flow. These sensors can provide
useful data to show if the system is performing optimally. Conversely, if the data shows that an avionics
system has a problem, the appropriate maintenance
can be scheduled at a suitable time. Ideally, predictive
maintenance data should indicate how much time
the airline has before there the avionics system will
experience a significant decrease in performance or,
in worst case, a complete failure.
The sensors used to monitor each aircraft system
are connected to electronic units, commonly referred
to as Flight Data Acquisition Systems (FDAU), that
are dedicated to collecting the data for analysis. An
example of an FDAU is shown in Figure 1. Some of
the desired system data is already monitored by the
aircraft avionics and can be transmitted to the FDAU
via a data bus, such as ARINC-429.
After being sent to the FDAU, the predictive maintenance data can be stored on removable media such
as compact flash drives, or can be transmit over a
network such as ACARS or Wi-Fi. FDAUs can also host
integrated processing modules to support onboard
computation, reducing the amount of data that needs
to be transmitted or stored.
There are a range of options for accessing the data.
An advantage of transmitting predictive maintenance
data over ACARS is that it becomes available for analysis virtually in real-time. Unfortunately, this approach
can be prohibitively expensive if there is a lot of data
to transmit. Similarly, data can be transmitted over
a cellular network, which, in addition to also being

Extreme Weather
3.08%

National
Aviation
System Delay
31.25%

Air Carrier
Delay
28.54%

Security Delay
0.19%
Aircraft Arriving Late
36.95%
quite expensive, requires a strong and reliable signal
to be effective. One alternative is to store the captured sensor data on removable compact flash modules. While these storage devices can support large
quantities of data, they need to be physically removed
from the aircraft prior to analysis. Lastly, the data
can be transmitted off the aircraft via Wi-Fi after it
arrives at the gate.
After the sensor data from the FDAU is made available, it needs to be combined with a variety of airline operational and maintenance data derived from
other sources and formats, including paper and PDF
documents. All of this data needs to be collected and
consolidated so that it can be analyzed. But first, it
must be checked for erroneous or missing data in a
process called "data cleaning." This time consuming
and painstaking process is essential for checking
the data and correcting any errors before the data
is analyzed.
In recent years, the aircraft industry has seen a
growing trend to use Artificial Intelligence (AI) and
Machine Learning (ML) to analyze the cleaned data
in order to identify any anomalies that show whether a
component or system is not performing correctly. The
resulting information can be used to plan the suspect
component's removal, so that it can either be tested or
replaced. At this point in the predictive maintenance
process testing, a component, even if known to exhibit
anomalous behavior, might be determined to have "No
Fault Found" (NFF), since it didn't fail. This situation
causes a quandary for airlines as they learn to optimize
the potential of predictive maintenance, since data

Air carrier delays
accounted for 28%
of aircraft delays
from 2009 to 2019.
Source: Bureau of
Transportation
Statistics, 2019, Airline
On-Time Statistics
and Delay Causes.

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Aircraft Maintenance Technology

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