Matteo Corbetta

Member since: Dec 20, 2019, NASA Ames Research Center

Fatigue Crack Growth in Aluminum Lap Joint - PHM Data Challenge 2019

A dataset shared by Matteo Corbetta, updated on Dec 20, 2019


Contributing Author(s) :
Yongming Liu , Tishun Peng

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 dataset is split in training and validation to facilitate the application of data-driven methods.

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Dataset structure and main info: please see ReadMe file.

In case you use this dataset to publish research in conference proceedings and journals, please cite the papers recommended in the ReadMe file.

The data is organized by folder structures. The parent folder contains one file and two folders. The file is named “Read Me.docx” and has all the information for the testing description and data explanation. One folder is named “training” and contains all training data sets. The other folder is named “validation” and contains all validation data set. The folders are organized by specimens, named T1-T6 in the training folder and T7-T8 in the validation folder. All formatting is explained in detail this file.

For any questions, contact this resource's administrator: NDC-mcorbet1


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  • Matteo Corbetta 1 year, 2 months

    This dataset was generated at Arizona State University by Prof. Yongming Liu, Dr. Tishun Peng, and their collaborators. Acknowledgements and relevant publications using this dataset are available in the ReadMe file.

    The dataset was used for the PHM Data Challenge for the 2019 Conference on Prognostics and Health Management. Other than the dataset authors, the following people helped put together the 2019 PHM data challenge and make the dataset publicly available.

    Matteo Corbetta and Portia Banerjee from NASA Ames,
    Kurt Doughty from Collins Aerospace,
    Kai Goebel from PARC, and
    Scott Clements from Lockheed Martin

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