EngineAD: A Real-World Vehicle Engine Anomaly Detection Dataset
This provides a realistic dataset for developing robust anomaly detection solutions in the automotive industry, though it is incremental as it builds on existing methods with new data.
The authors tackled the lack of real-world benchmarks for anomaly detection in transportation by introducing EngineAD, a dataset from 25 commercial vehicles over six months, and found that simple classical methods like K-Means and One-Class SVM often outperform deep learning approaches in segment-based evaluations.
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.