EngineAD: A Real-World Vehicle Engine Anomaly Detection Dataset

The progress of Anomaly Detection (AD) in safety-critical domains, such as transportation, is severely constrained by the lack of large-scale, real-world benchmarks. To address this, we introduce EngineAD, a novel, multivariate dataset comprising high-resolution sensor telemetry collected from a fleet of 25 commercial vehicles over a six-month period. Unlike synthetic datasets, EngineAD features authentic operational data labeled with expert annotations, distinguishing normal states from subtle indicators of incipient engine faults. We preprocess the data into 300-timestep segments of 8 principal components and establish an initial benchmark using nine diverse one-class anomaly detection models. Our experiments reveal significant performance variability across the vehicle fleet, underscoring the challenge of cross-vehicle generalization. Furthermore, our findings corroborate recent literature, showing that simple classical methods (e.g., K-Means and One-Class SVM) are often highly competitive with, or superior to, deep learning approaches in this segment-based evaluation. By publicly releasing EngineAD, we aim to provide a realistic, challenging resource for developing robust and field-deployable anomaly detection and anomaly prediction solutions for the automotive industry.

Please cite the following papers if you have used this dataset in your work:

1. H. Hojjati, C. Roth, R. Woods, K. Sills, and N. Armanfard, “EngineAD: A real-world vehicle engine anomaly detection dataset,” in Proc. 3rd Workshop on Automated Spatial and Temporal Anomaly Detection (ASTAD) at AAAI, Springer, 2026.

2. H. Hojjati, M. Sadeghi, and N. Armanfard, “Multivariate time-series anomaly detection with temporal self-supervision and graphs: Application to vehicle failure prediction,” in Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track (ECML PKDD), vol. 14175, pp. 239–254, Springer, 2023, doi: 10.1007/978-3-031-43430-3_15.