A framework is presented for data mining in multivariate time series collected over hours of ship operation to extract vessel states from the data. The measurements made by a ship monitoring system lead to a collection of time-organized in-service data. Usually, these time series datasets are big, complicated, and highly dimensional. The purpose of time-series data mining is to bridge the gap between a massive database and meaningful information hidden behind the data. An important aspect of the framework proposed is selecting relevant variables, eliminating unnecessary information or noises, and extracting the essential features of the problem so that the vessel behavior can be identified reliably. Principal component analysis (PCA) is employed to address the issues of multicollinearity in the data and dimensionality reduction. The data mining approach itself is established on unsupervised data clustering using self-organizing map (SOM) and k-means, and k-nearest neighbors search (k-NNS) for searching and recovering specific information from the database. As a case study, the results are based on onboard monitoring data of the Norwegian University of Science and Technology (NTNU) research vessel, “Gunnerus.” The scope of this work is limited to detecting ship maneuvers. However, it is extendable to a wide range of smart marine applications. As illustrated in the results, this approach is effective in identifying the prior unknown states of the ship with acceptable accuracy.
Multivariate Time Series Data Mining in Ship Monitoring Database
Contributed by the Ocean, Offshore, and Arctic Engineering Division of ASME for publication in the JOURNAL OF OFFSHORE MECHANICS AND ARCTIC ENGINEERING. Manuscript received November 18, 2016; final manuscript received June 9, 2017; published online August 16, 2017. Assoc. Editor: Marcelo R. Martins.
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Abbasi Hoseini, A., and Steen, S. (August 16, 2017). "Multivariate Time Series Data Mining in Ship Monitoring Database." ASME. J. Offshore Mech. Arct. Eng. December 2017; 139(6): 061304. https://doi.org/10.1115/1.4037293
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