Marine Engine Fault Dataset: Open-Access Data under Controlled Reference and Fault Scenario Conditions

2026-07-21

AI Summary

This study addresses the lack of open datasets in marine engine predictive maintenance that combine controlled fault experiments, well-defined operating conditions, and system-level measurements. The authors conducted bench tests on a turbocharged, intercooled three-cylinder marine diesel engine, covering normal operation across 30–90% load and five physical fault types: cooling water pump cavitation, air filter clogging, intercooler fouling, fuel injector clogging, and increased turbocharger exhaust backpressure. High-dimensional, multi-source time-series data were synchronously collected under these conditions. This dataset is the first to integrate controlled fault injection, multi-load operation, and comprehensive system-level sensing on a real marine platform. It demonstrates physical consistency between data and expected fault mechanisms, with distinct signatures across varying fault severities, thereby providing a high-quality, structured, and reusable open benchmark for anomaly detection and fault diagnosis research.

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.

Problem

• marine engine
• predictive maintenance
• fault dataset
• open-access data
• anomaly detection

Innovation

• marine engine fault dataset
• controlled fault injection
• multi-sensor time-series
• anomaly detection benchmark
• predictive maintenance