Our recent paper 'Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics' was featured on the cover of the Data, Volume 6, January 2021!
A new paper entitled "Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics" written by Manuel Arias Chao, Chetan Kulkarni, Kai Goebel and Olga Fink has been published.
Title: Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics
Authors: Manuel Arias Chao, Chetan Kulkarni, Kai Goebel and Olga Fink
Abstract
A key enabler of intelligent maintenance systems is the ability to predict the remaining useful lifetime (RUL) of its components, i.e., prognostics. The development of data-driven prognostics models requires datasets with run-to-failure trajectories. However, large representative run-to-failure datasets are often unavailable in real applications because failures are rare in many safety-critical systems. To foster the development of prognostics methods, we develop a new realistic dataset of run-to-failure trajectories for a fleet of aircraft engines under real flight conditions. The dataset was generated with the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) model developed at NASA. The damage propagation modelling used in this dataset builds on the modelling strategy from previous work and incorporates two new levels of fidelity. First, it considers real flight conditions as recorded on board of a commercial jet. Second, it extends the degradation modelling by relating the degradation process to its operation history. This dataset also provides the health, respectively, fault class. Therefore, besides its applicability to prognostics problems, the dataset can be used for fault diagnostics.

The Chair of Intelligent Maintenance Systems focuses on developing intelligent algorithms to improve performance, reliability and availability of complex industrial assets and making the maintenance more cost efficient. For more information visit our webpage