Proceedings of the Thirty-second (2022) International Ocean and Polar Engineering Conference
Shanghai, 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

Research on Ship Structure Characteristic Signal Recognition Based on Machine Learning Method
Hao-yun Tang1, Zi-chen He1, Xian-ku Zhang1, Qian Wan2
(1.Navigation College, Dalian Maritime University, Dalian, Liaoning, China;
2. Dalian Shipbuilding Industry Company, Dalian, Liaoning, China).

International Ship and Marine Structure Design Conference to provethe feasibility of monitor ship motion and structural stress response. In
this conference, the basic design framework of hull structure
monitoring system was also discussed. In order to simplify thearrangement of signal transmission lines further, Lynch(2006)
developed a wireless ship structure monitoring system for a high-speed
catamaran by using the wireless communication technology. The data
monitored by this monitoring system were used effectively into thelocal structure design optimization of catamarans. In China, theresearch on hull structure monitoring technology also developed rapidly.
Liu(2002) arranged a monitoring system in a large oil tanker for IranianNational Petroleum Company (NITS). The beam type sensors at main
deck of tanker were applied to measure hull longitudinal deformation inwaves. Subsequently, Zhang(2007) took a conventional bulk carrier as
the monitoring object to study the hull structure arrangement by using
some optical fiber sensors. Considering the structural features of bulkcarrier, a general scheme for sensor arrangement was given finally inthe bulk carrier. Then, Jin(2010) optimized the ship monitoring planand designed an intelligence system for container ships further. In orderto realize the rapid assessment of hull structure condition under ship
real-time monitoring, Jia(2011) studied the arrangement of hullstructure monitoring points and developed an online hull structureassessment system by combining fiber grating raster technology with
database technology. Ren(2017) then optimized further the real-timemonitoring and assessment system by using the finite element analysis.
Combined with the actual ship monitoring data, Zhou(2019) classified
the reasons for the monitoring sensor faults in real-time monitoring andgave a basic identification method. In order to forecast local structural
fatigue failure, Zuo(2019) and Lou(2021) both studied the development
process of potential fatigue damage and also gave some feasible plans
for monitoring hot spot stress in hull structure.

ABSTRACT
In order to improve the effectiveness of ship structural monitoring inlong term, the autonomous recognition for ship structure characteristic
signal is studied using machine learning technology. Combining Fast
Fourier filtering with back propagation neural network, an intelligent
signal recognition method is developed to identify ship structure
characteristic signals in complex marine environments. The feasibilityof signal recognition algorithm is proved by the monitored signal froma model test. The autonomous recognition of structural composite
signals at different monitored positions is then realized. In addition, theinfluence of multidimensional signal input and learning library size
torward signal recognition accuracy is further analyzed. The fluctuationof signal recognition rate is also observed in different frequency
thresholds. Finally, the related signal recognition parameters for generalapplication scenarios are given.

KEY WORDS: hull structure; structure characteristic signal; machinelearning; neural network;

INTRODUCTION,
With the development of large-scale and high-speed ship, thecharacteristics of hull structural condition in complex marine
environment have attracted more and more attention to ship designers.
In order to observe and study the fluctuation characteristics of hull
structural condition exactly, ship structure monitoring technology wasdeveloped gradually.
In the early stage of ship monitoring research, Lindemann(1978) firstly
designed a simplified hull surveillance system for improving structuralsafety in rough weather. However, the accuracy of surveillance system
is relatively low, due to the limitation of monitoring technology at thattime. The ship monitoring system was gradually optimized with therapid development of computer technology and signal transmission
technology, and Hjelme(1997) successfully observed hull structurehydro elastic fluctuation by using some optical fiber sensors. A ship
comprehensive monitoring system (1997) was then shown in

At present, the hull structure monitoring technology has become
matured. The monitoring signal date from full-scale ship and smallscale model is gradually sufficient in abundant test. However, there arestill some obstacles standing on the way for intelligent ships, especiallylack of studies on the monitoring signal identification and reproduction.
In fact, the fast identification technology towards ship structure
characteristic signal is critical for the intelligent goal that autonomous

3583