Research on fault diagnosis of marine engines based on partial domain adaptation

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Journal of Dalian Maritime University ›› 2026, Vol. 52 ›› Issue (1) : 52-64.

GUO Yu^*1，DAI Jun¹，ZHANG Jundong²，SUN Bin²

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Abstract

The safe and stable operation of marine engines is critical to national security and maritime traffic safety. While numerous deep learning methods have been extensively studied for intelligent fault detection, the complex operating conditions of marine engines—such as non-stationary states including variable loads—often lead to prevalent domain shift problems in practical fault diagnosis tasks. This significantly degrades the performance of conventional deep learning approaches. Using a specific marine engine as a case study, we constructed partial-set fault diagnosis scenarios under varying operating conditions. To address the challenge of missing fault labels in the target operating condition, we propose a knowledge transfer approach from source to target operating conditions. A novel Multi-scale and Multi-view Domain Adversarial Network (MMDAN) is designed and experimentally validated using marine engine data. Experimental results demonstrate that the proposed method achieves an average diagnostic accuracy of 96.58%. Furthermore, in partial-set transfer tasks across different operating conditions, MMDAN exhibits superior diagnostic performance compared to other state-of-the-art learning models.

Key words

/ marine engine；transfer learning；partial domain adaptation；fault diagnosis；adversarial training

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GUO Yu, DAI Jun, ZHANG Jundong, SUN Bin. Research on fault diagnosis of marine engines based on partial domain adaptation[J]. Journal of Dalian Maritime University. 2026, 52(1): 52-64

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