We used satellite altimetry data to investigate the Kuroshio Current because of the higher resolution and wider range of observations. In previous studies, satellite absolute geostrophic velocities were used to study ...We used satellite altimetry data to investigate the Kuroshio Current because of the higher resolution and wider range of observations. In previous studies, satellite absolute geostrophic velocities were used to study the spatiotemporal variability of the sea surface velocity field along the current, and extraction methods were employed to detect the Kuroshio axes and paths. However, sea surface absolute geostrophic velocity estimated from absolute dynamic topography should be regarded as the geostrophic component of the actual surface velocity, which cannot represent a sea surface current accurately. In this study, mathematical verification between the climatic absolute geostrophic and bin-averaged drifting buoy velocity was established and then adopted to correct the satellite absolute geostrophic velocities. There were some differences in the characteristics between satellite geostrophic and drifting buoy velocities. As a result, the corrected satellite absolute geostrophic velocities were used to detect the Kuroshio axis and path based on a principal-component detection scheme. The results showed that the detection of the Kuroshio axes and paths from corrected absolute geostrophic velocities performed better than those from satellite absolute geostrophic velocities and surface current estimations. The corrected satellite absolute geostrophic velocity may therefore contribute to more precise day-to-day detection of the Kuroshio Current axis and path.展开更多
主元分析(principal component analysis,PCA)是一种有效的数据分析方法,在故障诊断与状态监测方面已得到广泛应用.多元指数加权移动平均–主元分析(multivariate exponentially weighted moving average principal component analysis,...主元分析(principal component analysis,PCA)是一种有效的数据分析方法,在故障诊断与状态监测方面已得到广泛应用.多元指数加权移动平均–主元分析(multivariate exponentially weighted moving average principal component analysis,MEWMA–PCA)方法用于解决PCA不能有效检出微小故障的问题.本文深入研究了MEWMA–PCA中EWMA影响主元分析进行故障检测的机制,导出了MEWMA–PCA可检出微小故障的原因.本文确定了MEWMA–PCA中遗忘因子λ、单传感器故障幅值和迟延时间三者的关系,并进行了数值仿真和火电厂磨煤机组运行状态的仿真实验.实验结果验证了MEWMA–PCA中EWMA提高PCA的监测性能的机制,并给出了根据系统实际要求来选取合适的遗忘因子值,从而在规定的时间内检出微小故障的实例.展开更多
基金The National Science and Technology Major Project of the Ministry of Science and Technology of China under contract No.2018YFF01014100the National Programme on Global Change and Air-Sea Interaction under contract No.GASI-IPOVAI-01-05the NSFC-Shandong Joint Fund for Marine Science Research Centers under contract No.U1606405
文摘We used satellite altimetry data to investigate the Kuroshio Current because of the higher resolution and wider range of observations. In previous studies, satellite absolute geostrophic velocities were used to study the spatiotemporal variability of the sea surface velocity field along the current, and extraction methods were employed to detect the Kuroshio axes and paths. However, sea surface absolute geostrophic velocity estimated from absolute dynamic topography should be regarded as the geostrophic component of the actual surface velocity, which cannot represent a sea surface current accurately. In this study, mathematical verification between the climatic absolute geostrophic and bin-averaged drifting buoy velocity was established and then adopted to correct the satellite absolute geostrophic velocities. There were some differences in the characteristics between satellite geostrophic and drifting buoy velocities. As a result, the corrected satellite absolute geostrophic velocities were used to detect the Kuroshio axis and path based on a principal-component detection scheme. The results showed that the detection of the Kuroshio axes and paths from corrected absolute geostrophic velocities performed better than those from satellite absolute geostrophic velocities and surface current estimations. The corrected satellite absolute geostrophic velocity may therefore contribute to more precise day-to-day detection of the Kuroshio Current axis and path.
文摘主元分析(principal component analysis,PCA)是一种有效的数据分析方法,在故障诊断与状态监测方面已得到广泛应用.多元指数加权移动平均–主元分析(multivariate exponentially weighted moving average principal component analysis,MEWMA–PCA)方法用于解决PCA不能有效检出微小故障的问题.本文深入研究了MEWMA–PCA中EWMA影响主元分析进行故障检测的机制,导出了MEWMA–PCA可检出微小故障的原因.本文确定了MEWMA–PCA中遗忘因子λ、单传感器故障幅值和迟延时间三者的关系,并进行了数值仿真和火电厂磨煤机组运行状态的仿真实验.实验结果验证了MEWMA–PCA中EWMA提高PCA的监测性能的机制,并给出了根据系统实际要求来选取合适的遗忘因子值,从而在规定的时间内检出微小故障的实例.
