EngineAD

EngineAD dataset for training and testing anomaly detection algorithms on engine sensor data. To request access:

1. Fill out this form.
2. After submitting the form, visit the dataset page on Borealis Dataverse, select all the files, and click the "Request Access" button.

Citation

If you use the EngineAD dataset or this codebase in your research, please cite the following papers:

1. EngineAD: A Real-World Vehicle Engine Anomaly Detection Dataset

@inproceedings{hojjati2026enginead,
  title={EngineAD: A Real-World Vehicle Engine Anomaly Detection Dataset},
  author={Hojjati, Hadi and Roth, Christopher and Woods, Rory and Sills, Ken and Armanfard, Narges},
  booktitle={Proceedings of the 3rd Workshop on Automated Spatial and Temporal Anomaly Detection (ASTAD) at AAAI},
  series={Communications in Computer and Information Science},
  year={2026},
  publisher={Springer},
}

2. Multivariate Time-Series Anomaly Detection with Temporal Self-supervision and Graphs

@inproceedings{hojjati2023multivariate,
  title={Multivariate Time-Series Anomaly Detection with Temporal Self-supervision and Graphs: Application to Vehicle Failure Prediction},
  author={Hojjati, Hadi and Sadeghi, Mohammadreza and Armanfard, Narges},
  booktitle={Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track (ECML PKDD)},
  series={Lecture Notes in Computer Science},
  volume={14175},
  pages={239--254},
  year={2023},
  publisher={Springer},
  doi={10.1007/978-3-031-43430-3_15}
}