EngineAD: A Real-World Vehicle Engine Anomaly Detection Dataset

TL;DR

EngineAD is a large, real-world vehicle engine anomaly detection dataset with expert labels, designed to advance anomaly detection research in transportation.

Contribution

We introduce EngineAD, a real-world, labeled dataset with benchmark results, highlighting challenges in cross-vehicle generalization and classical methods' competitiveness.

Findings

Significant variability in anomaly detection performance across vehicles.

Classical methods like K-Means and One-Class SVM perform strongly.

EngineAD provides a realistic benchmark for automotive anomaly detection.

Abstract

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.