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This paper presents the largest publicly available, non-simulated, fleet-wideaircraft flight recording and maintenance log data for use in predicting partfailure and maintenance need. We present 31,177 hours of flight data across28,935 flights, which occur relative to 2,111 unplanned maintenance eventsclustered into 36 types of maintenance issues. Flights are annotated as beforeor after maintenance, with some flights occurring on the day of maintenance.Collecting data to evaluate predictive maintenance systems is challengingbecause it is difficult, dangerous, and unethical to generate data fromcompromised aircraft. To overcome this, we use the National General AviationFlight Information Database (NGAFID), which contains flights recorded duringregular operation of aircraft, and maintenance logs to construct a part failuredataset. We use a novel framing of Remaining Useful Life (RUL) prediction andconsider the probability that the RUL of a part is greater than 2 days. Unlikeprevious datasets generated with simulations or in laboratory settings, theNGAFID Aviation Maintenance Dataset contains real flight records andmaintenance logs from different seasons, weather conditions, pilots, and flightpatterns. Additionally, we provide Python code to easily download the datasetand a Colab environment to reproduce our benchmarks on three different models.Our dataset presents a difficult challenge for machine learning researchers anda valuable opportunity to test and develop prognostic health management methods

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