Abstract

Abstract: Statistical filter based sensor and data acquisition (DAQ) fault detection is presented in this study. The parameters of a large-scale data set of ship performance and navigation information are considered as statistical distributions and principal component analysis (PCA) is used to identify the hidden structure of the same data set. This data set relates to a specific operating region of the main engine, where ship performance and navigation conditions can be linearized. The structure derived under PCA is further investigated to identify the respective sensor and DAQ fault situations as the main contribution. That is done by projecting the same data set into the respective principal components, where a new set of ship performance and navigation parameters is derived. Then, the respective parameter variance values of the new data set are calculated and the thresholds that relate to the same variance values for detecting sensor and DAQ fault situations are derived. Finally, the data set of ship performance and navigation information is analyzed through these fault thresholds and the successful results on identifying complex fault situations are presented in this study. Hence, this approach can be used to develop advanced sensor and DAQ fault detection and isolation methodologies of ship performance and navigation monitoring systems.

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