Remaining Useful Lifetime Estimation

Engine degradation simulation was carried out using C-MAPSS. Four different were sets simulated under different combinations of operational conditions and fault modes. Records several sensor channels to characterize fault evolution. The data set was provided by the Prognostics CoE at NASA Ames.

The generation of data-driven prognostics models requires the availability of datasets with run-to-failure trajectories. In order to contribute to the development of these methods, the dataset provides a new realistic dataset of run-to-failure trajectories for a small fleet of aircraft engines under realistic flight conditions. The damage propagation modelling used for the generation of this synthetic dataset builds on the modeling strategy from previous work . The dataset was generated with the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dynamical model. The data set is been provided by the Prognostics CoE at NASA Ames in collaboration with ETH Zurich and PARC.

The PRONOSTIA (also called FEMTO) bearing dataset consists of 17 accelerated run-to-failures on a small bearing test rig. Both acceleration and temperature data was collected for each experiment. The dataset was used in the 2012 IEEE Prognostic Challenge. The dataset is from FEMTO-ST Institute in France.

Bearing acceleration data from three run-to-failure experiments on a loaded shaft. The data set was provided by the Center for Intelligent Maintenance Systems (IMS), University of Cincinnati.