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An improved control-limit-based principal component analysis method for condition monitoring of marine turbine generators

机译：一种改进的基于控制限制的主要分析方法，用于船用汽轮机发生器的状态监测

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摘要

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.
机译：海洋涡轮发电机的安全运行是行业和学术界至关重要的关注。监控海上涡轮发电机的健康状况始终是重要的。润滑油通常承载有关涡轮机操作条件的丰富信息。现有的监测系统中使用了涡轮机的各种油参数。然而，其中许多以逆检测结果相互冲突。因此，它应该消除冗余油参数以进行高效的条件监测。尽管许多研究研究解决了使用主成分分析（PCA）的冗余特征减少问题，但PCA专为具有线性关系而设计的功能，船用汽轮机发生器监控不是这种情况。本文提出了一种新的非线性分析方法，改进的基于控制限制的PCA，从海上汽轮机发电机的油参数中提取不同的失效指标。该方法的贡献是，结合了热灵统统计和Q统计以计算PCA的固定控制限制。通过提出的方法显着提高了改进的PCA对非线性处理非线性的能力。实验验证表明，使用该方法提取的故障指示器比关于故障检测精度的现有监测指标更有效。

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  • 来源
    《Journal of marine engineering and technology》 |2020年第4期|249-256|共8页
  • 作者

    Yang Kun; Hu Biao; Malekian Reza; Li Zhixiong;

  • 作者单位

    Wuhan Univ Technol Reliabil Engn Inst Sch Energy & Power Engn Wuhan Peoples R China|Wuhan Univ Technol Natl Engn Res Ctr forWater Transport Safety Wuhan Peoples R China;

    Wuhan Univ Technol Reliabil Engn Inst Sch Energy & Power Engn Wuhan Peoples R China|Wuhan Univ Technol Natl Engn Res Ctr forWater Transport Safety Wuhan Peoples R China;

    Univ Pretoria Dept Elect Elect & Comp Engn Pretoria South Africa;

    Ocean Univ China Sch Engn Qingdao Peoples R China|Univ Wollongong Sch Mech Mat Mechatron & Biomed Engn Wollongong NSW Australia;

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