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Marine Engine Centered Localized Models for Sensor Fault Detection under Ship Performance Monitoring

机译：船舶性能监测下以船舶发动机为中心的传感器故障检测局部模型

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Abstract: Sensor fault detection under marine engine centered localized models of an engine propeller combinator diagram is presented in this study. The proposed approach consists of two detection levels to identify of sensor fault situations in an onboard data acquisition system of a vessel. Each parameter in ship performance and navigation data can have a realistic data range (i.e. a threshold relates to the variance), where the parameter can vary. If the sensor reads a value beyond this parameter range, then that data point is categorized as a sensor fault situation by the first fault detection level. However, some sensor faults are located within this data range and that cannot identify by this detection level. Such complex sensor fault situations are detected by the second fault detection level by considering the proposed localized models. These localized models are derived with respect to the operating regions of an engine-propeller combinator diagram, where the respective data points are clustered by Gaussian mixture models with an expectation maximization algorithm. Each data cluster is examined through principal component analysis and projected into the bottom principal component to identify such complex sensor fault situations. A data set of ship performance and navigation information of a selected vessel is used through these sensor fault detection levels and the successful results on identifying such sensor fault situations are also presented in this study.
机译：摘要：本研究提出了一种以船用发动机为中心的发动机螺旋桨组合器图局部化模型下的传感器故障检测方法。所提出的方法包括两个检测级别，以识别船舶的机载数据采集系统中的传感器故障情况。船舶性能和导航数据中的每个参数都可以具有实际的数据范围（即，阈值与差异有关），其中参数可以变化。如果传感器读取的值超出此参数范围，则该数据点将被第一故障检测级别归类为传感器故障情况。但是，某些传感器故障位于此数据范围内，无法通过此检测级别识别。通过考虑建议的局部模型，第二故障检测级别可检测到此类复杂的传感器故障情况。这些局部模型是针对发动机-螺旋桨组合器图的运行区域而得出的，在该图中，各个数据点由具有期望最大化算法的高斯混合模型聚类。通过主成分分析检查每个数据集群，并将其投影到底部的主成分中，以识别此类复杂的传感器故障情况。通过这些传感器故障检测级别使用所选船舶的船舶性能和导航信息的数据集，并且在这项研究中还提供了识别此类传感器故障情况的成功结果。

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