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中文题名:

 

基于状态监测数据的船舶柴油机健康状态诊断方法研究

    

姓名:

 王瑞涵    

学号:

 104971180259    

保密级别:

 公开    

论文语种:

 chi    

学科代码:

 082402    

学科名称:

 工学 - 船舶与海洋工程 - 轮机工程    

学生类型:

 博士    

学校:

 武汉理工大学    

院系:

 船海与能源动力工程学院    

专业:

 船舶与海洋工程    

研究方向:

 机舱智能管理,故障诊断    

第一导师姓名:

 陈辉    

第一导师院系:

 船海与能源动力工程学院    

完成日期:

 2023-03-20    

答辩日期:

 2023-05-20    

中文关键词:

 

船舶柴油机 ; 故障诊断 ; 健康状态评估 ; 机器学习 ; 弱监督学习

    

中文摘要:

船舶柴油机是船舶的“心脏”,是船舶主要的推进装置和发电装备。船舶柴油机系统安全、稳定、高效的运行对于提升船舶航行的安全性和可靠性具有重要的意义。因此,研究船舶柴油机的健康状态诊断方法具有重要意义,可以及早发现异常工况和精准实现故障定位,最终保障船舶高效运营和降低船舶航运成本。本文以船舶柴油机系统为研究对象,利用采集的柴油机状态信息数据,借助机器学习与深度学习算法,开展基于数据驱动的的船舶柴油机健康状态诊断方法研究,建立一套完备的船舶柴油机健康状态诊断方案。由于实际采集数据中存在故障样本数据少、各工况样本数量不平衡、有标签数据稀疏和未知工况样本识别错误等问题对传统的强监督学习模型造成了巨大的挑战。因此,本文从半监督学习和自监督学习出发,提出针对状态信息数据质量和数量弱依赖的解决范式,使得柴油机健康状态智能诊断模型在样本信息缺失情形下仍能取得令人满意的诊断效果。主要开展如下几个方面的研究工作:

(1)为实现船舶柴油机异常状态的检测,引入机器学习算法,提出一种基于流形学习和孤立森林结合的船舶柴油机异常状态检测方法。由于船舶柴油机的状态监测数据是多维度数据,因此通过流形学习来提取有效的数据特征,实现对原始数据的降维。基于孤立森林算法,在利用大量正常工况数据的情况下,训练并构建孤立森林模型中的多个子森林检测器,用于实现对目标设备的故障检测。根据孤立森林模型中得到的平均路径长度,基于聚类思想提出了一种故障阈值,通过该阈值可以使所提出故障检测方案的诊断结果具有可解释性。在 Matlab/Simulink 环境下建立大型船舶二冲程柴油机模型,通过与台架数据进行对比来验证仿真模型的精确性。利用柴油机仿真数据对不同故障检测方案性能进行比较,表明所提基于流形学习和孤立森林结合故障检测方案比其它检测方案具有更好的性能,实现98.5%的故障检测率和3%的故障虚警率。

(2)提出一种新型深度学习网络结构—随机卷积神经网络(Random Convolutional Neural Networks, RCNNs),用于实现船舶柴油机的故障状态识别。所提出的RCNNs模型由多个单一的卷积神经网络模型集成,无需结合工程实践经验与专家知识,可直接通过多层卷积计算和池化操作对柴油机的状态信息数据实现特征提取。同时,在RCNNs模型中应用Adabound优化器,使得学习率自适应变化,从而加快模型参数的更新速度。通过注意力机制层(Attention Mechanism)来实现对各个单一网络模型诊断结果的融合。所提出的RCNNs模型用于对船舶柴油机振动信号进行分析。实验证明,RCNNs模型克服了基于机器学习和单一卷积神经网络的故障诊断方法局限性,摆脱了对人工特征设计的依赖,减少了诊断结果的不确定性,并且有较好的诊断性能,能够有效地对船舶柴油机进行故障诊断。

(3)面对柴油机状态信息数据中各类别下数据量不平衡的问题,提出了一种基于距离和概率拓扑图的图卷积网络(Graph Convolutional Network based on Distance and Probability Topological Graphs, DPGCN)用于非均衡数据集下的船舶柴油机故障诊断。基于欧式距离和Kullback-Leibler散度将采集的状态信息样本转化为距离拓扑图和概率拓扑图。然后,利用图卷积学习对状态信息数据拓扑图中的样本自身特征以及邻近样本特征进行聚合和提取,可为非均衡数据集下智能诊断模型的训练提供样本特征信息和数据集的空间结构信息。经多层图卷积学习之后,获得了基于距离拓扑图和概率拓扑图的两个图嵌入特征矩阵,通过设计注意力机制自适应地计算两个特征矩阵的权重,并融合至一个特征矩阵中,挖掘可用于故障诊断任务中更深层次的故障特征信息。通过仿真模型和实船数据两个案例表明,提出的DPGCN模型可以在非均衡数据集下实现比其它算法更高的精确率、召回率及准确率。

(4)面对采集数据中标签数据少,无法对未存在于训练集的样本进行分类的问题,基于自监督对比学习,提出一种分布外泛化自监督对比学习框架(Self-Supervised Contrastive Learning Framework with an Out-of-Distribution Classifier,SCLODC)。SCLODC模型可以通过对比学习在大量无标签数据中学习到有价值的特征表征,然后利用少量标签数据对模型微调使之适用于机械故障诊断任务。SCLODC模型中确定了一组合适的数据增强组合策略来生成正例样本对,以辅助从无标签数据样本中提取有用的特征。其次,在该框架中设计了一种基于类别概率和置信水平的分布外泛化分类器(Out-of-Distribution Classifier),通过输出置信水平来评估当前的分类结果,以识别未知故障类型的样本。通过四种不同的工业故障诊断案例,结果表明SCLODC模型可以有效地从大量无标签样本中提取有价值的特征信息,并可诊断出未在训练集中出现过的新故障工况样本,获得比其他机器学习、深度学习和已有的自监督学习框架更高的诊断精度。

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中图分类号:

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