船舶燃气轮机推进系统跨工况并发故障诊断框架

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大连海事大学学报 ›› 2025, Vol. 51 ›› Issue (4) : 58-66.

桑培晟¹，谭阳辉^*1，高强¹，张均东²，高雅¹，张云洲¹

作者信息 +

A cross-condition concurrent fault diagnosis framework for the marine gas turbine propulsion system

SANG Peisheng¹, TAN Yanghui^*1, GAO Qiang¹, ZHANG Jundong², GAO Ya¹, ZHANG Yunzhou¹

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摘要

针对传统船舶机械故障诊断方法通常仅关注单一故障场景、缺乏并发故障跨域诊断能力的问题，本文提出一种基于全连接多标签域适应神经网络(FMANN)的智能故障诊断框架。首先，引入联合域适应方法，实现了不同工况的故障特征迁移，有效解决了目标工况小样本故障诊断难题；其次，通过多标签分类方法捕捉各类故障间的复杂关联，进而实现并发故障的迁移诊断；最后，对比分析该框架下几种常见域适应方法的性能表现，并利用某船舶燃气轮机推进系统的退化数据集验证了该框架的有效性和鲁棒性。

Abstract

Aiming at the problem that traditional ship machinery fault diagnosis methods usually only focus on a single fault scenario and lack the ability to diagnose concurrent faults across domains, an intelligent fault diagnosis framework based on fully connected multilabel domain adaptive neural network (FMANN) was proposed. Firstly, the joint domain adaptation method was introduced to achieve the fault feature transfer under different working conditions, effectively solving the problem of small sample fault diagnosis under target working conditions. Secondly, by introducing a multilabel classification method to capture the complex relationships among different faults, the migration diagnosis of concurrent faults has been achieved. Finally, the performance of several common domain adaptation methods under this framework was compared and analyzed, and the effectiveness and robustness of the proposed framework were verified by using the degradation dataset of a certain ship gas turbine propulsion system.

导出引用

桑培晟, 谭阳辉, 高强, 张均东, 高雅, 张云洲. 船舶燃气轮机推进系统跨工况并发故障诊断框架[J]. 大连海事大学学报. 2025, 51(4): 58-66

SANG Peisheng, TAN Yanghui, GAO Qiang, ZHANG Jundong, GAO Ya , ZHANG Yunzhou. A cross-condition concurrent fault diagnosis framework for the marine gas turbine propulsion system[J]. Journal of Dalian Maritime University. 2025, 51(4): 58-66

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