Marine Engine Fault Dataset: Open-Access Data under Controlled Reference and Fault Scenario Conditions
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Computer Science > Machine Learning
arXiv:2607.19444 (cs)
[Submitted on 21 Jul 2026]
Title:Marine Engine Fault Dataset: Open-Access Data under Controlled Reference and Fault Scenario Conditions
View a PDF of the paper titled Marine Engine Fault Dataset: Open-Access Data under Controlled Reference and Fault Scenario Conditions, by Ahmad BahooToroody and 4 other authors
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Abstract:Open-access datasets for marine-engine predictive maintenance remain scarce, particularly those from controlled fault experiments with documented operating conditions, subsystem-level interventions and system-level measurements. This work presents the Marine Engine Fault Dataset, an openly available dataset from a turbocharged, intercooled three-cylinder marine diesel engine operated on a testbed under both reference and fault-scenario conditions. The experimental campaign combined a reference-performance program across the 30-90% load range with scenario-based tests in which abnormal conditions were introduced after stabilized fault-free operation, enabling controlled comparison between baseline and fault-affected behaviour. Five anomaly classes were implemented through physical interventions affecting major engine subsystems: cooling-water pump cavitation, compressor air-filter clogging, air-cooler fouling, injection-valve nozzle clogging and turbine degradation induced through increased exhaust-side restriction. The released data comprise multi-sensor time-series of operating, thermal, pressure, flow and combustion-related variables, with a separate reference-performance record and metadata for structured reuse. Technical validation shows that the reference measurements remain physically coherent across the operating range and that the imposed anomalies produce interpretable response patterns consistent with the affected subsystems, including progressively distinguishable behaviour where different severities were implemented. By combining controlled fault realization, multi-load operation and system-level measurements within a real marine-engine platform, the dataset provides a well-documented benchmark for anomaly detection, fault diagnosis, degradation modelling and related condition-monitoring studies in maritime machinery.
| Comments: | submitted to a journal and currently is under review, 34 pages, 13 Figures |
| Subjects: | Machine Learning (cs.LG); Signal Processing (eess.SP); Systems and Control (eess.SY) |
| Cite as: | arXiv:2607.19444 [cs.LG] |
| (or arXiv:2607.19444v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.19444
arXiv-issued DOI via DataCite (pending registration)
|
Submission history
From: Mohammad Mahdi Abaei Dr [view email][v1] Tue, 21 Jul 2026 10:18:57 UTC (14,675 KB)
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