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An overview of data veracity issues in ship performance and navigation monitoring in relation to data sets collected from a selected vessel is presented in this study. Data veracity relates to the quality of ship performance and navigation parameters obtained by onboard IoT (internet of things). Industrial IoT can introduce various anomalies into measured ship performance and navigation parameters and that can degrade the outcome of the respective data analysis. Therefore, the identification and isolation process of such data anomalies can play an important role in the outcome of ship performance and navigation monitoring. In general, these data anomalies can be divided into sensor and data acquisition (DAQ) faults and system abnormal events. A considerable amount of domain knowledge is required to detect and classify such data anomalies, therefore data anomaly detection layers are proposed in this study for the same purpose. These data anomaly detection layers are divided into several levels: preliminary and advanced levels. The outcome of a preliminary anomaly detection layer with respect to ship performance and navigation data sets of a selected vessel is presented with the respective data handling challenges as the main contribution of this study.

This study proposes marine engine centered data analytics as a part of the ship energy efficiency management plan (SEEMP) to overcome the current shipping industrial challenges. The SEEMP enforces various emission control measures, where ship energy efficiency should be evaluated by collecting vessel performance and navigation data. That information is used to develop the proposed data analytics that are implemented on the engine-propeller combinator diagram (i.e. one propeller shaft with its own direct drive main engine). Three marine engine operating regions from the initial data analysis are noted under the combinator diagram and the proposed data analytics (i.e. data clustering methodology) to capture the shape of these regions are implemented. That consists of implementing the Gaussian Mixture Models (GMMs) to classify the most frequent operating regions of the marine engine. Furthermore, the Expectation Maximization (EM) algorithm is used to calculate the respective parameters of the GMMs. This approach can also be seen as a data clustering algorithm that facilitated by an iterative process for capturing each operating region of the marine engine (i.e. the combinatory diagram) with the respective mean and covariance values. Hence, these data analytics can be used in the SEEMP to monitor the performance of a vessel with respect to the marine operating regions. Furthermore, it is expected to develop advanced mathematical models of ship performance monitoring under these operational regions of the marine engine as the future work.

This study proposes marine engine centered data analytics as a part of the ship energy efficiency management plan (SEEMP). The SEEMP enforces various emission control measures to improve ship energy efficiency by considering vessel performance and navigation data. The proposed data analytics is developed in the engine-propeller combinator diagram (i.e., one propeller shaft with a direct drive main engine). Three operating regions from the initial data analysis are under the combinator diagram noted to capture the shape of these regions by the proposed data analytics. The data analytics consists of implementing Gaussian mixture models (GMMs) to classify the most frequent operating regions of the main engine. Furthermore, the expectation maximization (EM) algorithm calculates the parameters of GMMs. This approach, also named data clustering algorithm, facilitates an iterative process for capturing the operating regions of the main engine (i.e., in the combinatory diagram) with the respective mean and covariance matrices. Hence, these data analytics can monitor ship performance and navigation conditions with respect to engine operating regions as a part of the SEEMP. Furthermore, development of advanced mathematical models for ship performance monitoring within the operational regions (i.e., data clusters) of marine engines is expected.