TITLE: A Large-Scale Annotated Multivariate Time Series Aviation Maintenance Dataset from the NGAFID 

ABSTRACT: This paper presents the largest publicly available, non-simulated, fleet-wide
aircraft flight recording and maintenance log data for use in predicting part
failure and maintenance need. We present 31,177 hours of flight data across
28,935 flights, which occur relative to 2,111 unplanned maintenance events
clustered into 36 types of maintenance issues. Flights are annotated as before
or after maintenance, with some flights occurring on the day of maintenance.
Collecting data to evaluate predictive maintenance systems is challenging
because it is difficult, dangerous, and unethical to generate data from
compromised aircraft. To overcome this, we use the National General Aviation
Flight Information Database (NGAFID), which contains flights recorded during
regular operation of aircraft, and maintenance logs to construct a part failure
dataset. We use a novel framing of Remaining Useful Life (RUL) prediction and
consider the probability that the RUL of a part is greater than 2 days. Unlike
previous datasets generated with simulations or in laboratory settings, the
NGAFID Aviation Maintenance Dataset contains real flight records and
maintenance logs from different seasons, weather conditions, pilots, and flight
patterns. Additionally, we provide Python code to easily download the dataset
and a Colab environment to reproduce our benchmarks on three different models.
Our dataset presents a difficult challenge for machine learning researchers and
a valuable opportunity to test and develop prognostic health management methods
This paper presents the largest publicly available, non-simulated, fleet-wide
aircraft flight recording and maintenance log data for use in predicting part
failure and maintenance need. We present 31,177 hours of flight data across
28,935 flights, which occur relative to 2,111 unplanned maintenance events
clustered into 36 types of maintenance issues. Flights are annotated as before
or after maintenance, with some flights occurring on the day of maintenance.
Collecting data to evaluate predictive maintenance systems is challenging
because it is difficult, dangerous, and unethical to generate data from
compromised aircraft. To overcome this, we use the National General Aviation
Flight Information Database (NGAFID), which contains flights recorded during
regular operation of aircraft, and maintenance logs to construct a part failure
dataset. We use a novel framing of Remaining Useful Life (RUL) prediction and
consider the probability that the RUL of a part is greater than 2 days. Unlike
previous datasets generated with simulations or in laboratory settings, the
NGAFID Aviation Maintenance Dataset contains real flight records and
maintenance logs from different seasons, weather conditions, pilots, and flight
patterns. Additionally, we provide Python code to easily download the dataset
and a Colab environment to reproduce our benchmarks on three different models.
Our dataset presents a difficult challenge for machine learning researchers and
a valuable opportunity to test and develop prognostic health management methods
A Large-Scale Annotated Multivariate Time Series Aviation Maintenance Dataset from the NGAFID
http://arxiv.org/abs/2210.07317v1