# EngineAD: A Real-World Vehicle Engine Anomaly Detection Dataset

arXiv: https://arxiv.org/abs/2603.25955 · HTML: https://mlsanity.com/paper/2603.25955/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 26 Mar 2026.

## Builds on (most cited first, 5 of 5)

- [COPOD: Copula-Based Outlier Detection](https://mlsanity.com/paper/2009.09463/) (2020)
- [Deep Learning for Time Series Anomaly Detection: A Survey](https://mlsanity.com/paper/2211.05244/) (2022)
- [Self-Supervised Anomaly Detection in Computer Vision and Beyond: A Survey and Outlook](https://mlsanity.com/paper/2205.05173/) (2022)
- [Dive into Time-Series Anomaly Detection: A Decade Review](https://mlsanity.com/paper/2412.20512/) (2024)
- [Foundation Models for Time Series: A Survey](https://mlsanity.com/paper/2504.04011/) (2025)

## Abstract (arXiv)

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.