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Research Papers: Offshore Technology

Multivariate Time Series Data Mining in Ship Monitoring Database

[+] Author and Article Information
Afshin Abbasi Hoseini

Mem. ASME
Department of Marine Technology,
Norwegian University of Science and
Technology (NTNU),
Trondheim 7491, Norway
e-mails: afshin.abbasi-hoseini@ntnu.no;
afshin.abbasi@engineer.com

Sverre Steen

Department of Marine Technology,
Norwegian University of Science and
Technology (NTNU),
Trondheim 7491, Norway
e-mail: sverre.steen@ntnu.no

1Corresponding author.

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.

J. Offshore Mech. Arct. Eng 139(6), 061304 (Aug 16, 2017) (8 pages) Paper No: OMAE-16-1147; doi: 10.1115/1.4037293 History: Received November 18, 2016; Revised June 09, 2017

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.

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Figures

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Fig. 1

Outline of steps of data mining in ship measurement data

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Fig. 2

Samples of (a) rudder angle and (b) surge velocity; original data (blue) and median-filtered data (red)

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Fig. 3

Illustration of PCA

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Fig. 4

Features extracted from Gunnerus time series after normalization

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Fig. 5

Architecture of SOM and input vectors for clustering ship multivariate data; different colors show distinctive groups of features on the map

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Fig. 6

The weight distances between neighboring neurons in the feature map; the blue hexagons represent the neurons. The red lines connect neighboring neurons. The colors in the regions containing the red lines indicate the distances between neurons. The darker colors represent larger distances, and the lighter colors represent smaller distances.

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Fig. 7

Time series clustered of Gunnerus; different colors indicate distinct ship maneuvers: (a) rudder angle, (b) yaw rate, (c) propeller speed, and (d) surge velocity

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Fig. 8

Silhouette plot for the data clustered of Gunnerus

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Fig. 9

Samples of the information extracted for maneuvers of (a) straight-line motion with high-speed and high power and (b) sharp starboard side turning

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