The safe operation of marine turbine generators is a crucial concern in industries and academics. It is always important to monitor the health status of marine turbine generators. The lubricant oil usually carries abundant information on the turbine operation conditions. Various oil parameters of the turbines have been used in the existing monitoring systems. However, many of them conflict with each other by contrary detection results. Hence, it should eliminate the redundant oil parameters for efficient condition monitoring. Although many research studies addressed the redundant feature reduction issue using principal component analysis (PCA), PCA is designed for features with a linear relationship, which is not the case in marine turbine generator monitoring. This paper proposes a new nonlinear analysis method, the improved control-limit based PCA, to extract distinct failure indicators from the oil parameters of marine turbine generators. The contribution of this method is that the Hotelling statistic and Q statistic are combined to calculate a fixed control limit for PCA. The ability of the improved PCA to dealing with nonlinearity has been significantly enhanced by the proposed method. Experimental validation demonstrates that the extracted failure indicator using the proposed method is more effective than existing monitoring indexes with respect to fault detection accuracy.

中文翻译：

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一种改进的基于控制极限的船用汽轮发电机状态监测主成分分析方法

船用涡轮发电机的安全运行是工业和学术界的一个关键问题。监控船用涡轮发电机的健康状态始终很重要。润滑油通常携带有关涡轮机运行条件的丰富信息。涡轮机的各种油参数已用于现有的监测系统。然而，它们中的许多因相反的检测结果而相互冲突。因此，它应该消除冗余的油参数以进行有效的状态监测。尽管许多研究使用主成分分析 (PCA) 解决了冗余特征减少问题，但 PCA 是为具有线性关系的特征而设计的，这在船用涡轮发电机监测中并非如此。本文提出了一种新的非线性分析方法，改进的基于控制极限的 PCA，从船用涡轮发电机的油参数中提取不同的故障指标。这种方法的贡献是结合了 Hotelling 统计量和 Q 统计量来计算 PCA 的固定控制限。所提出的方法显着增强了改进的PCA处理非线性的能力。实验验证表明，使用所提出的方法提取的故障指标在故障检测精度方面比现有监测指标更有效。所提出的方法显着增强了改进的PCA处理非线性的能力。实验验证表明，使用所提出的方法提取的故障指标在故障检测精度方面比现有监测指标更有效。所提出的方法显着增强了改进的PCA处理非线性的能力。实验验证表明，使用所提出的方法提取的故障指标在故障检测精度方面比现有监测指标更有效。