船舶发动机故障数据集：在受控参考和故障场景条件下的开放获取数据
Marine Engine Fault Dataset: Open-Access Data under Controlled Reference and Fault Scenario Conditions
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中文总结 AI 辅助
该研究提出船舶发动机故障数据集，通过在实际船舶发动机平台结合受控故障实现、多负载运行和系统级测量获取数据，包括多传感器时间序列等，为海上机械相关研究提供记录良好的基准。
中文摘要 AI 辅助
用于船舶发动机预测性维护的开放获取数据集仍然稀缺，特别是那些来自具有记录运行条件、子系统级干预和系统级测量的受控故障实验的数据集。这项工作提出了船舶发动机故障数据集，这是一个公开可用的数据集，来自一台涡轮增压、中冷三缸船用柴油发动机，该发动机在试验台上在参考和故障场景条件下运行。实验活动将30-90%负载范围内的参考性能程序与基于场景的测试相结合，在稳定的无故障运行后引入异常条件，从而能够对基线和受故障影响的行为进行受控比较。通过对主要发动机子系统进行物理干预，实现了五个异常类别：冷却水泵气蚀、压缩机空气滤清器堵塞、空气冷却器结垢、喷油阀喷嘴堵塞以及通过增加排气侧限制引起的涡轮退化。发布的数据包括运行、热、压力、流量和燃烧相关变量的多传感器时间序列，以及单独的参考性能记录和用于结构化重用的元数据。技术验证表明，参考测量在整个运行范围内保持物理一致性，并且施加的异常产生了与受影响子系统一致的可解释响应模式，包括在实施不同严重程度时逐渐可区分的行为。通过在实际船舶发动机平台内结合受控故障实现、多负载运行和系统级测量，该数据集为海上机械的异常检测、故障诊断、退化建模和相关状态监测研究提供了一个记录良好的基准。
英文摘要
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.
