The environmental impact of aviation is enormous given the fact that
in the US alone there are nearly 6 million flights per year of commercial aircraft.
This situation has driven numerous policy and procedural measures to help develop
environmentally friendly technologies which are safe and affordable and reduce the
environmental impact of aviation. However, many of these technologies require significant
initial investment in newer aircraft fleets and modifications to existing regulations
which are both long and costly enterprises. We propose to use an anomaly
detection method based on Virtual Sensors to help detect overconsumption of fuel in
aircraft which relies only on the data recorded during flight of most existing commercial
aircraft, thus significantly reducing the cost and complexity of implementing this
method. The Virtual Sensors developed here are ensemble-learning regression models
for detecting the overconsumption of fuel based on instantaneous measurements
of the aircraft state. This approach requires no additional information about standard
operating procedures or other encoded domain knowledge. We present experimental
results on three data sets and compare five different Virtual Sensors algorithms. The
first two data sets are publicly available and consist of a simulated data set from a
flight simulator and a real-world turbine disk.We show the ability to detect anomalies
with high accuracy on these data sets. These sets contain seeded faults, meaning that
they have been deliberately injected into the system. The second data set is from realworld
fleet of 84 jet aircraft where we show the ability to detect fuel overconsumption
which can have a significant environmental and economic impact. To the best of our
knowledge, this is the first study of its kind in the aviation domain.