Proceedings of the Thirty-second (2022) International Ocean and Polar Engineering ConferenceShanghai, China, June 5–10, 2022
Copyright © 2022 by the International Society of Offshore and Polar Engineers (ISOPE)
ISBN 978-1-880653-81-4; ISSN 1098-6189

www.isope.org

Cleaning and Expansion of Ship Speed Data Based on Cloud Model Theory
Yuanhang Hou1#, Kai Kang2, Huande Wang1, Yeping Xiong3
1: Naval Architecture and Ocean Engineering College, Dalian Maritime University, Dalian, China
2: Dalian COSCO-KHI Ship Engineering Co. Ltd, China
3: Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, UK

ship's internal system, speed will always fluctuate to some extent.
Although the uncertainty amplitude caused by speed perturbation issmall in most cases, in the ship intelligent design and operation mode,
continuous iterative calculation and coupling with other parameters willcause large deviation to the system response. Therefore, on the basis of
the massive speed data obtained by navigation instruments, theredundant information is filtered, and the effective information that candirectly express the statistical characteristics is extracted and mined. As
the indispensable core knowledge to express navigation performance, it
is of great significance for ship intelligent design and operation.
“Data driven” is a more cutting-edge engineering cognitive logic, byobserving the real state of objective world elements and constantly
expanding the known database to the cognitive. Ly et al. (2016) used adata mining method to effectively analyse shipping data and realised
track clustering based on Automatic Identification System (AIS) data.
Suyuthi (2012) used the traditional method of a statistical model as initial
distribution, the alternative method with ice load peak value as randomvariable and the asymptotic method based on one-minute maximum,
respectively, to predict the short-term extreme value of the local ice load,
among which the asymptotic method is more accurate. Chi et al. (2019)
established a speed optimisation data-driven model based on neuralnetwork theory to predict the energy efficiency of ships in the Arcticregion. The results show that the error of the data-driven prediction
model is less than 5%, which shows the advantages of the data-driven
application in energy efficiency index predictions. Cha (2016) applied
data mining technology to analyse the performance relationship between
ocean and ship, established a cloud platform, compared ship
performance in all its aspects and proposed an upgraded data mining
algorithm. The results showed that the algorithm had high accuracy. Guo(2021) introduced big data of ship traffic flow into the intelligencenavigation trajectory of unmanned aerial vehicles and conducted path
planning.
In sum, in the field of shipping, the main research directions of big datatechnology are track clustering, ship scheduling, ice area parameterprediction and principal scale analysis. However, it is used less often inship performance optimisation. Reasonably, big data technology canmake use of a large amount of redundant and scattered information toimprove the reliability of ship design schemes. Due to the differences intime, place or equipment, collected data samples are multi-source andheterogeneous. Inevitably, data have disturbances, are noisy and aremissing, which cannot effectively reflect the real probability density

ABSTRACTAs one of the most important navigation parameters, speed directly
determines the accuracy of evaluating sailing performance. In ship
intelligence design and operation, considering the uncertainty of speed
fluctuation caused by internal and external uncontrollable factors, therobustness of design and decision-making results can be effectively
improved. Data mining can extract key information from a large amountof irregular data. In order to achieve the data expansion of ship speed
time series and complete the pre-work of the robust design of the overall
performance of the ship, this paper extracted normal statisticalinformation of the full-scale ship speed time series. First, theinterpolation method was used to make the speed data have a unified timeinterval. In order to obtain stable data periods, wavelet decomposition
was used to achieve noise reduction of data, which were then segmented
by the extreme point labelling method. Second, cloud model theory wasintroduced for data expansion. Forward and reverse cloud generators
were used to expand data into a single stable section. Finally, a statisticalnormality test was used to verify whether the expansion data strictly
obeyed normal distribution. The results showed that the expansion datastrictly obeyed normal distribution, which could provide the data
foundation and data-driven model technical support for the subsequent
ship design and navigation strategy formulation.
KEY WORDS: Ship speed; Data mining; Cloud model; Wavelet
decomposition; Normality test.

INTRODUCTION
As is typical of big data of the Internet of Things (IoT), shipping massive
time series data contains rich information resources, which cancomprehensively and effectively reflect the perturbation information of
the external environment and ship operation parameters. Therefore, theapplication of big shipping data in the field of ship intelligence design isbecoming increasingly in-depth. Driving the research of ship overalldesign and navigation strategy based on the navigation data can reduce
errors caused by the actual operation uncertainty, and so improve therobustness of the optimal design result.
In ship design and operation, speed, as one of the most importantnavigation parameters, directly determines the accuracy of evaluation ofsailing performance. Under the influence of external wind and waves and3654