基于多模态信息融合的船用柴油机数字孪生故障预警方法

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大连海事大学学报 ›› 2026, Vol. 52 ›› Issue (1) : 65-78.

孙佳文¹，任鸿翔^*1，杨晓¹，王德龙¹，潘明阳¹，韦德鉴²

作者信息 +

A digital twin-based fault early warning method for marine diesel engines based on multimodal information fusion

SUN Jiawen¹，REN Hongxiang^*1，YANG Xiao¹，WANG Delong¹，PAN Mingyang¹，WEI Dejian²

Author information +

文章历史 +

摘要

为实现船用柴油机运行状态的精准感知与故障有效预警，提出一种融合机理仿真与传感实测信息的多模态数字孪生方法。该方法通过引入性能退化校正机制构建高保真柴油机热力学仿真模型，设计融合多尺度卷积与注意力机制的特征提取网络，完成两类互补信息的深层特征提取与跨模态融合；以偏差度为预警指标，结合核密度估计实现性能参数阈值自学习，构建具备动态适应能力的故障预警机制。结合9L34DF型双燃料船用柴油机的运行数据，验证了该方法在实际工况下的有效性与适用性。

Abstract

To achieve accurate perception of the operational status and effective fault early warning of marine diesel engines, a multimodal digital twin method integrating mechanism simulation and sensor measurement information was proposed. This method introduced a performance degradation correction mechanism to construct a high-fidelity thermodynamic simulation model of diesel engines, designed a feature extraction network integrating multi-scale convolution and attention mechanisms, and accomplished deep feature extraction and cross-modal fusion of the two types of complementary information. Taking the deviation degree as the early warning index, the self-learning of performance parameter thresholds was realized combined with kernel density estimation, and a fault early warning mechanism with dynamic adaptability was constructed. The effectiveness and applicability of the proposed method under actual operating conditions were verified based on the operational data of the 9L34DF dual-fuel marine diesel engine.

导出引用

孙佳文, 任鸿翔, 杨晓, 王德龙, 潘明阳, 韦德鉴. 基于多模态信息融合的船用柴油机数字孪生故障预警方法[J]. 大连海事大学学报. 2026, 52(1): 65-78

SUN Jiawen, REN Hongxiang, YANG Xiao, WANG Delong, PAN Mingyang, WEI Dejian. A digital twin-based fault early warning method for marine diesel engines based on multimodal information fusion[J]. Journal of Dalian Maritime University. 2026, 52(1): 65-78

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