Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics

By Manuel Arias Chao, Chetan S. Kulkarni, Kai F. Goebel, Olga Fink. International Conference on Data Technologies and Applications, 2021

312 citations recorded by the source corpus.

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.

Subfields: Control and Systems Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/d15f621ef21d894276c60451130e03d73d3d67f4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Olga Fink
2. Kai F. Goebel
3. Manuel Arias Chao
4. Chetan S. Kulkarni

In Safety, Risk, Reliability and Quality

1. Olga Fink
2. Kai F. Goebel
3. Manuel Arias Chao

Citations in the corpus

• Fusing Physics-based and Deep Learning Models for Prognostics (2020, methodology): “Real flight conditions, as recorded on board commercial jets, were used as input to the CMAPSS model [23].”