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The Prognostics Data Repository is a collection of data sets that have been donated by universities, agencies, or companies. The data repository focuses exclusively on prognostic data sets, i.e., data sets that can be used for the development of prognostic algorithms. Most of these are time-series data from a prior nominal state to a failed state. The collection of data in this repository is an ongoing process.

*** This page is also mirrored at the Prognostics Health Management (PHM) Society website.***

Publications making use of databases obtained from this repository are requested to acknowledge both the assistance received by using this repository and the donators of the data. This will help others to obtain the same data sets and replicate your experiments. It also provides credit to the donators.

Users employ the data at their own risk. Neither NASA nor the donators of the data sets assume any liability for the use of the data, or any system developed using the data. 

If you have suggestions concerning the repository, e-mail chetan.s.kulkarni@nasa.gov or christopher.a.teubert@nasa.gov 

Data Sets

1. Algae Raceway

Experiments were conducted on 3 small raceways in which spirulina was inoculated. The growth and, ultimately, decline of the algae biomass was recorded along with several environmental parameters. Experiments were conducted by the Exobiology group at NASA Ames.

2. Carbon Fiber-Reinforced Polymer (CFRP) Composites

Run-to-failure experiments on CFRP panels with periodic measurements to capture internal damage growth under tension-tension fatigue. Monitoring data consists of lamb wave signals from a network of 16 piezoelectric (lead zirconate titanate – PZT) sensors and multiple triaxial strain gages. Additionally, periodic x-rays were taken to characterize internal damage as ground truth information. Three different layups were tested. Experiments were conducted at Stanford Structures and Composites Laboratory (SACL) in collaboration with the NASA Ames Research Center Prognostic Center of Excellence (PCoE).

3. Milling

Experiments on a milling machine for different speeds, feeds, and depth of cut. Records the wear of the milling insert, VB. The data set was provided by the UC Berkeley Emergent Space Tensegrities (BEST) Lab.

4. Bearings

Experiments on bearings. The data set was provided by the Center for Intelligent Maintenance Systems (IMS), University of Cincinnati.

  • Download: https://data.nasa.gov/download/brfb-gzcv/application%2Fzip
  • Download Mirror: https://phm-datasets.s3.amazonaws.com/NASA/4.+Bearings.zip 
  • Data Set Citation: J. Lee, H. Qiu, G. Yu, J. Lin, and Rexnord Technical Services (2007). IMS, University of Cincinnati. “Bearing Data Set”, NASA Prognostics Data Repository, NASA Ames Research Center, Moffett Field, CA

5. Batteries

Experiments on Li-Ion batteries. Charging and discharging at different temperatures. Records the impedance as the damage criterion. The data set was provided by the NASA Prognostics Center of Excellence (PCoE).

  • Download Mirror: https://phm-datasets.s3.amazonaws.com/NASA/5.+Battery+Data+Set.zip
  • Data Set Citation: B. Saha and K. Goebel (2007). “Battery Data Set”, NASA Prognostics Data Repository, NASA Ames Research Center, Moffett Field, CA

6. Turbofan Engine Degradation Simulation

Engine degradation simulation was carried out using the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS). Four different sets were simulated under different combinations of operational conditions and fault modes. This records several sensor channels to characterize fault evolution. The data set was provided by the NASA Ames Prognostics Center of Excellence (PCoE).

7. Prognostics Health Management 8 (PHM08) Challenge

Data from the data challenge competition held at the 1st international conference on Prognostics and Health Management (PHM08) is similar to the one posted above (see the Turbofan Engine Degradation Simulation data set) except the true Remaining Useful Life (RUL) values are not revealed. Users are expected to develop their algorithms using training and test sets provided in the package. The data set was provided by the NASA Prognostics Center of Excellence (PCoE).

  • Download: https://data.nasa.gov/download/nk8v-ckry/application%2Fzip
  • Data Set Citation: A. Saxena and K. Goebel (2008). “PHM08 Challenge Data Set”, NASA Prognostics Data Repository, NASA Ames Research Center, Moffett Field, CA
  • Evaluation Link: Currently Unavailable
  • Notes:
    • Results should be formatted as a column vector of RULs in a text file.
    • Evaluation is limited to only one trial per day.

8. Insulated-Gate Bipolar Transistor (IGBT) Accelerated Aging

Preliminary data from thermal overstress accelerated aging using the aging and characterization system. The data set contains aging data from 6 devices, one device aged with DC gate bias and the rest aged with a squared signal gate bias. Several variables are recorded and, in some cases, high-speed measurements of gate voltage, collector-emitter voltage, and collector current are available. The data set is provided by the NASA Prognostics Center of Excellence (PCoE).

9. Trebuchet

Trajectories of different types of balls launched from a trebuchet with varying counter weights. Flights were filmed and extraction routines calculated position of data. Both raw video data and extracted trajectories are provided. Geometry and physical properties of the trebuchet are available.

  • Download Mirror: Data is currently unavailable for download directly. NASA is working to restore direct download capabilities. In the meantime, if you would like access to the data, please contact christopher.a.teubert@nasa.gov
  • Data Set Citation: B. Morton. Sentient Corporation. “Trebuchet Data Set”, NASA Prognostics Data Repository, NASA Ames Research Center, Moffett Field, CA

10. FEMTO Bearing

Experiments on bearings’ accelerated life tests provided by FEMTO-ST Institute, Besançon, France. More information can be found here.

  • Download Mirror: https://phm-datasets.s3.amazonaws.com/NASA/10.+FEMTO+Bearing.zip
  • Data Set Citation: “FEMTO Bearing Data Set”, NASA Prognostics Data Repository, NASA Ames Research Center, Moffett Field, CA
  • Publication Citation: P. Nectoux, R. Gouriveau, K. Medjaher, E. Ramasso, B. Morello, N. Zerhouni, C. Varnier. PRONOSTIA: An Experimental Platform for Bearings Accelerated Life Test. Institute of Electrical and Electronics Engineers (IEEE) International Conference on Prognostics and Health Management, Denver, CO, USA, 2012

11. Randomized Battery Usage

Batteries are continuously cycled with randomly generated current profiles. Reference charging and discharging cycles are also performed after a fixed interval of randomized usage to provide reference benchmarks for battery state of health.

12. Capacitor Electrical Stress

Capacitors were subjected to electrical stress under three voltage levels, i.e. 10V, 12V, and 14V. Data Set contains Electrical Impedance Spectroscopy (EIS) data as well as Charge/Discharge Signal data.

13. Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET) Thermal Overstress Aging

Run-to-failure experiments on Power MOSFETs under thermal overstress.

  • Data Set Reference Document: Currently offline – email christopher.a.teubert@nasa.gov
  • Download Mirror: https://phm-datasets.s3.amazonaws.com/NASA/13.+MOSFET+Thermal+Overstress+Aging.zip
  • Data Set Citation: J. R. Celaya, A. Saxena, S. Saha, and K. Goebel “MOSFET Thermal Overstress Aging Data Set”, NASA Prognostics Data Repository, NASA Ames Research Center, Moffett Field, CA
  • Publication Citation: J. R. Celaya, A. Saxena, S. Saha, and K. Goebel, “Prognostics of Power MOSFETs under Thermal Stress Accelerated Aging using Data-Driven and Model-Based Methodologies,” in the Proceedings of the Annual Conference of the Prognostics and Health Management Society, (Montreal QC, Canada), September 2011.

14. Capacitor Electrical Stress-2

Capacitors were subjected to electrical stress at 10V.

15. High-Intensity Radiated Field (HIRF) Battery

Battery Data collected from the Experiments on the Edge 540 Aircraft in a HIRF Chamber.

  • Data Set Reference Document: Currently offline – email christopher.a.teubert@nasa.gov
  • Download Mirror: https://phm-datasets.s3.amazonaws.com/NASA/15.+HIRF+Battery+Data+Set.zip
  • Data Set Citation: C. Kulkarni, E. Hogge, C. Quach and K. Goebel “HIRF Battery Data Set”, NASA Prognostics Data Repository, NASA Ames Research Center, Moffett Field, CA
  • Publication Citation: Edward F. Hogge, Brian M. Bole, Sixto L. Vazquez, Jose Celaya,”Verification of a Remaining Flying Time Prediction System for Small Electric Aircraft”, Annual Conference of the Prognostics and Health Management, PHM 2015

16. Small Satellite Power Simulation

Data collected from the simulated experiments on small satellite BP930 batteries using the MACCOR system.

17. Turbofan Engine Degradation Simulation-2

The generation of data-driven prognostics models requires the availability of data sets with run-to-failure trajectories. To contribute to the development of these methods, the data set provides a new realistic data set 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 data set builds on the modeling strategy from previous work. The data set was generated with the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dynamical model. The data set has been provided by the NASA Prognostics Center of Excellence (PCoE) in collaboration with ETH Zurich and PARC.

18. Fatigue Crack Growth in Aluminum Lap Joint

Fatigue experiments were conducted on aluminum lap-joint specimens, and lamb wave signals were recorded for each specimen at several time points (i.e., defined as number of cycles in fatigue testing). Signals from piezo actuator-receiver sensor pairs were reported and it was observed that these signals were directly related to the crack lengths developed during fatigue testing. Optical measurements of surface crack lengths are also provided as the ground truth. The data set is split in training and validation to facilitate the application of data-driven methods. This data set was generated at Arizona State University by Prof. Yongming Liu, Dr. Tishun Peng, and their collaborators. The data set was used for the Prognostics Health Management (PHM) Data Challenge for the 2019 Conference on Prognostics and Health Management. Other than the data set authors, the following people helped put together the 2019 PHM data challenge and make the data set publicly available. Matteo Corbetta and Portia Banerjee (KBR, Inc, NASA Ames), Kurt Doughty (Collins Aerospace), Kai Goebel (PARC), and Scott Clements (Lockheed Martin).

  • Download: https://data.nasa.gov/download/awzu-cpt8/application%2Fzip
  • Data Set Citation: Peng T, He J, Xiang Y, Liu Y, Saxena A, Celaya J, Goebel K. Probabilistic fatigue damage prognosis of lap joint using Bayesian updating. Journal of Intelligent Material Systems and Structures. 2015 May;26(8):965-79.
  • Publication Citation: He J, Guan X, Peng T, Liu Y, Saxena A, Celaya J, Goebel K. A multi-feature integration method for fatigue crack detection and crack length estimation in riveted lap joints using Lamb waves. Smart Materials and Structures. 2013 Sep 4;22(10):105007.

19. CNC Milling Machine

Remaining Useful Life (RUL) estimation for high-speed CNC milling machine cutters using dynamometer, accelerometer, and acoustic emission data. This data was used in the 2010 Prognostics Health Management (PHM) Society Data Competition.

20. Anemometer

Data set for cup anemometers. This data was used in the 2011 Prognostics Health Management (PHM) Society Data Competition.

Thank you Andreas Lövberg (RISE) for your help identifying some of the relevant data sets listed here.
https://phmsociety.org/phm_competition/2011-phm-society-conference-data-challenge/

 21. Accelerated Battery Life Testing

This data set presents accelerated-Li-ion battery lifecycle data focused on a large range of load levels and the characterization of the lifecycle of a battery pack composed of two 18650 battery cells. The lifecycle study is conducted with a total of 26 battery packs that are grouped by constant and random loading conditions, loading levels, and number of load-level changes. The data also includes load cycling on second-life batteries, where surviving cells from previously aged battery packs were assembled for second- life packs.

  • Download: https://data.nasa.gov/download/xg3n-ngei/application%2Fzip
  • Data Set Citation: Fricke, K., Nascimento, R., Corbetta, M., Kulkarni, C., & Viana, F. “Accelerated Battery Life Testing Dataset”, NASA Prognostics Data Repository, Probabilistic Mechanics Lab, University of Central Florida, and NASA Ames Research Center, Moffett Field, CA
  • Publication Citation: Fricke, K., Nascimento, R., Corbetta, M., Kulkarni, C., & Viana, F. (2023). Prognosis of Li-ion Batteries Under Large Load Variations Using Hybrid Physics-Informed Neural Networks. Annual Conference of the PHM Society, 15(1). https://doi.org/10.36001/phmconf.2023.v15i1.3463

This data set was generated from a custom-made testbed to cycle battery packs designed and developed by Kajetan Fricke, Renato Nascimento, and Professor Felipe Viana from the Probabilistic Mechanics Laboratory at the University of Central Florida (UCF). This work is the result of a collaboration between the Probabilistic Mechanics Lab at the University of Central Florida, and the Intelligent Systems Division Diagnostics & Prognostics Group at NASA Ames Research Center.