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Sustainable Maritime Transportation and Exploitation of Sea Resources – Rizzuto & Guedes Soares (eds)© 2012 Taylor & Francis Group, London, ISBN 978-0-415-62081-9

Development of an onboard decision support system for ship navigation under rough weather conditions

L.P. Perera, J.M. Rodrigues, R. Pascoal & C. Guedes SoaresCentre for Marine Technology and Engineering (CENTEC), Instituto Superior Tecnico, Technical University of Lisbon, Lisbon, Portugal

ABSTRACT: The paper describes the development of an onboard decision support system to support ship operation, in particular on decisions about ship handling in waves, which will contribute to vessel safety. The prototype system monitors several motion related parameters, and, by processing these data, provides the ship master with the information about the consequences of the different ship handling decisions. The paper describes the decision criteria and the approaches adopted for the calculation of the parameters that govern the master’s decisions. It describes the software that was developed to perform those calculations and to display in a user interface the advice to the master as well as the data acquisition and processing hardware that has been organized for the on board monitoring of motions and strains in the structure.

Later, Huss & Olander (1994) formulated a prototype of an on board based guidance and sur-veillance system for wave induced effects on ships, where a rate gyro and an accelerometer were used towards the local real-time estimation of the sea state in the form of a spectrum. Köse et al. (1995) proposed a scheme based on low cost equipments which, with special purpose developed software, resulted in an encompassing reliable system for stability monitoring and advising applicable to any ship. Payer, and Rathje, (2004) presented a onboard system thought for containership opera-tion in rough seas.

The present work deals with the development of an onboard decision support system for tacti-cal decisions of ship handling in waves, which ena-bles the master to improve ship performance while minimizing the likelihood of structural damage. The system now reported has been formulated in 2005, before the start of the EU Handling Waves project (http://www.mar.ist.utl.pt/handlingwaves/). It has some similar principles and solutions to the decision support system that has been developed for the operation of fishing vessels, as described by Rodrigues et al (2011).

More recently, Bitner-Gregersen, & Skjong (2009) and Nielsen & Jensen (2010) presented concepts of risk-based guidance of ships, which have some ideas that can be incorporated in future systems.

The system now reported besides monitoring in real time the actual ship responses, also predicts the near term motions and structural loads due

1 INTRODUCTION

The effect of waves in rough weather is one of the factors that most degrade a ship’s operational effi-ciency. Therefore, the tactical judgment involved in the ship handling decision process takes an essen-tial part in navigation. Rothblum et al. (2002) and Antão & Guedes Soares (2008) have shown that 75% to 96% of marine casualties have their ori-gins in some kind of human errors, where human errors are still one of the major causes of mari-time accidents (Guedes Soares, & Teixeira, (2001)). Therefore whenever the navigators can be helped with monitoring and decision support systems, a contribution is being given to safety.

The initial developments of onboard systems to aid the navigation in rough weather had been mainly concerned with structural integrity and equipment safety. Lindemann et al. (1977) devel-oped one such system by measuring the accelera-tions in six degrees of freedom and the stresses at a cross section. Hoffman (1980), considered a system using ship to shore communications along with charts for routing in heavy weather.

The work of Koyama et al. (1982) consisted of a computer based system capable of computing the mean period and the root mean square predic-tion of roll motion. The input component was a pendulum for measuring the ship motions and, given a pre-determined criterion, an alarm would fire in case of danger. Unfortunately the pendulum system proved unsatisfactory especially for high speeds so the results were shown to be unreliable.

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both to weather changes and to possible changes in course and speed by the shipmaster. It also includes a component of hull monitoring by strain gauges, which corresponds to a more established technology as described by Slaughter et al. (1997), which however is here integrated with the decision support system.

More specifically this paper deals with the develop-ment of monitoring devices able to accurately meas-ure the motions of the ship and the implementation of a Decision Support System (DSS) integrating the various elements required. The system’s architecture, working principles, dataflow, calculation procedures and equipment are herein described.

2 DECISION SUPPORT SYSTEM

Figure 1 shows the logical architecture of the DSS. The main modules are: Data Processing and Anal-ysis module, Structural Loads Estimator module, and the Sea Estimator module.

The Data Processing and Analysis module allows the monitoring in real-time of the ship motions and accelerations for arbitrary positions in the ship. The Structural Loads Estimator pro-vides estimation of the loads on the structure, assessed by different approaches.

The Sea Estimator Module uses the filtered and digitized accelerations and velocities associ-ated with ship motions, measured during the pre-vious minutes, and these are used to estimate the directional wave spectrum. This is achieved by the implementation of a Kalman filter based algorithm described by Pascoal & Guedes Soares (2009), which proved a better option than the approach considered initially, which is described in Pascoal & Guedes Soares (2008).

From this estimation of the spectrum, the sys-tem predicts the near term motions and structural loads due both to weather changes and to possible changes in course, taking for this a probabilistic approach in the form of root mean square values of key motion amplitude and acceleration levels.

These parameters are then checked against pre-defined operational safety criteria, which results in the construction of a polar plot on which the areas with dangerous combinations of ship course and speed are indicated. The prediction of motions and accelerations is done from the esti-mated directional wave spectra and the existence of pre-calculated motion transfer functions using a strip theory code. With these values, the criteria for operability and seasickness in NORDFORSK (1987) and in O’Halon and McCauley (1974) are assessed.

It has also been implemented the capability to check, for a given the sea state, the probability of occurrence of parametric rolling based on experi-mental and numerical results. A simple query to a database containing the results for the different combinations of ship speed and course was the methodology chosen.

The Structural Loads Estimator also uses the estimated spectrum and transfer functions for shear forces and bending moments to predict what would be the loads in the structure in the different options of course and speed decisions. It also includes a neural network model (Moreira and Guedes Soares, 2011) that uses as input the measured accelerations and motions at the vari-ous locations of the ship and produces as output the shear stresses and bending moments at selected locations, so that the ship master can have on-line information of the loads that the structure is being subjected.

In addition to the motion measurement equip-ment, a set of strain-gauge units are installed onboard, so as to provide a direct measurement of the strains in the structure, which can be compared with the predicted strains from the neural network model of the Structural Loads Estimator.

3 CRITERIA FOR SHIP OPERATIONAL SAFETY

The operability criterion correlates the type of work to be performed at a given location on the vessel with its maximum rms (root mean square) values of lateral acceleration, vertical acceleration and roll amplitude. The limit values according to NORDFORSK (1987) are listed in (Table 1).

The O’Hanlon and McCauley (1974) criterion for MSI (Motion Sickness Incidence) is defined as the percentage of people to experience seasickness during a period of two hours. It is governed by the following expressions:

MSI 100 0 erf a g) %z Mg) SI=μl (og /

.0 4.10±⎛

⎝⎜⎛⎛⎝⎝

⎞⎠⎟⎞⎞⎠⎠

⎛⎝⎜⎛⎛⎝⎝

⎞⎠⎟⎞⎞⎠⎠

∓ (1)Figure 1. DSS logical architecture.

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μ =MSI 1= 0 e0 819 2 32− +0 819 ( )ω10 eωlog 2 (2)

erfr erfrz

dzx

( )x = erfr ( )x = −⎛

⎝⎜⎛⎛

⎝⎝

⎞

⎠⎟⎞⎞

⎠⎠∫12 2

2

0πexp (3)

where az stands for the vertical acceleration at some point in the ship where one wishes to assess the incidence of motion sickness and, in (Eq. 2), ωe is the frequency of encounter.

It is crucial to point out that the above defined quantities are evaluated at some point in the struc-ture of the ship where it is significant to assess the motions. Therefore, there is a need to expand the linear motions from the centre of gravity, which are given by the raw transfer functions, into the absolute motions at the specific point of interest.

4 IMPLEMENTATION OF THE DECISION SUPPORT SYSTEM

Regarding the actual implementation, the system may be decomposed into 5 units as presented in Figure 2, where the software architecture is shown and the implementation tools are referred ( LABVIEW and C#).

The System Sensors are responsible for all motion measurements and the Wave Spectrum Estimation Program takes into action the task of estimating the real-time local sea state. The User Interface allows access to the polar plot as described in Section 1, it permits the graphical monitoring of all variable values that govern the calculations taking place, and constitutes the tool by which the developer may perform analysis and troubleshooting.

The Ship Operability Estimation Program is an application responsible for the computation of the predicted near term motions for the current and other possible combinations of speed and course, from the knowledge of the estimated spectrum main parameters.

Finally, the Main Program constitutes the kernel of the system’s dataflow. It collects and stores all real-time sensor and estimated spectrum data, it is

also responsible for communicating with the Ship Operability Estimation Program by sending the spectrum parameters and receiving a set of m × n matrices to be forwarded to the User Interface. Here m is the number of discretized possible speeds of advance and n the number of discretized possible ship course directions. These matrices are populated with zeros and ones, thus providing a mapping to implement on a polar plot, where a one classifies a combination of speed and course which results in an undesirable behaviour of the ship for the current sea state, in light of the criteria defined in Section 2.

4.1 Wave spectrum estimation program

The Wave Spectrum Estimation Program runs under the LABVIEW Real-Time (RT) operating system. This unit implements a high-speed itera-tive procedure for estimating the ocean wave direc-tional spectrum from the vessel motion data. It uses as input the measurements from motion sen-sors and provides spectral updates under quickly changing weather conditions.

The Kalman filtering algorithm, for iterative harmonic detection, and frequency domain vessel response data in the form of transfer functions, are used in the estimation process. The output is the estimated directional spectrum parameters: signifi-cant wave height, mean period and mean direction. More details on this subject may be found in the references given in Section 1 concerning the real-time spectrum estimation.

4.2 User interface

The view of the User Interface on the laptop com-puter is presented in Figure 3. The interface consists of 9 parts: data management, application manage-ment, GPS display, channel test, danger zones dis-play, statistics data, estimated spectrum display, loads conditions display, and motion signals dis-play. These sections are presented by separate tabs:

• Data management: The data management sec-tion is responsible for collecting sensor and GPS data (accelerometer, wave height measurement

Table 1. RMS criterion.

Description of work

��x P3, ��x2,P x P4,

m/s2 m/s2 deg

Light manual work 0.20 g 0.10 g 6.0Heavy manual work 0.15 g 0.07 g 4.0Intellectual work 0.10 g 0.05 g 3.0Transit passengers 0.05 g 0.04 g 2.5Cruise liner 0.02 g 0.03 g 2.0

Figure 2. Software architecture.

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sensor, inclinometer, angular rate sensor, strain gauge sensors). The sensor data is then saved to an external hard drive.

• Application management: The application management section enables the operator to stop, reboot and restart individual processes and programs. These processes and programs are: User Interface, CompactRIO and EtherCAT (see Section 4) units, and Wave Spectrum Estimator.

• GPS display: The GPS display is responsible for read and display services of data collected from the GPS sensor.

• Channel test: This section is designed to read and write uncelebrated data from the different channels in the data acquisition system. Any problems with read and write operations with Data Acquisition (DAQ) channels can be evalu-ated in this unit.

• Danger zones: The main objective of the danger zones section is to display the results calculated by the Operability Assessment Program. A polar plot constructed by mapping the matrices previ-ously defined is presented.

• Statistics display: This section consists of statisti-cal information about the accumulated data that has been collected by the ship sensors. However, currently this unit is under development.

• Estimated spectrum display: Relates to the wave spectrum estimation which has been done by the Kalman filter algorithm that is running under the LabVIEW RT platform on the Wave Spec-trum Estimation Program. An isometric view of the bi-dimensional spectrum is displayed in this window.

• Load conditions display: The ship’s hull stress conditions that are measured by the strain gauge sensors are displayed in this section. High strain values are monitored by the program and a warn-ing light will also be displayed on this section.

• Motion signal display: The calibrated ship motions that are measured by the sensors are displayed in this section. The ship surge, sway and heave accelerations, pitch and roll angles

and yaw rate are displayed in the top area of this section. The measured wave height is displayed on the bottom area of this section.

4.3 Ship operability estimation program

The Ship Operability Estimation Program imple-ments the criteria checking defined in Section 2. Based on the estimated spectrum parameter it constructs a JONSWAP spectrum and computes the necessary quantities previously defined. Con-trary to the remainder of the system, this tool is not part of the LABVIEW en vironment (see Fig. 2), but is rather a .net standalone applica-tion. The communication with the Main Program is done through continuously updated input and output files.

In Figure 4 the architecture of this application is presented, where it can be seen the inclusion of a parametric rolling occurrence check. This check has yet to be correctly implemented, although the logic has already been set to work. It consists on a query which is done to a database of simulations/tests done on the particular vessel for which the occurrence of this phenomenon is likely to be expected. These simulations/tests are not yet avail-able and this fact constitutes the cause to which this subject is not discussed further in this paper.

5 EXPERIMENTAL PLATFORM

The DSS prototype has been installed on a Ro-Ro ship with Lpp = 214.0 m and B = 32.0 m. Another prototype is currently being set up on a container vessel with Lpp = 117.6 m and B = 20.2 m.

The system has been put to work on the Ro-Ro ship and it has been collecting data from the motions and calculations results. Once enough

Figure 3. Laptop on the bridge with user interface.

Figure 4. Ship operability estimation program architecture.

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data have been collected the same will then be used to calibrate and validate the system. In this section, a brief presentation of the individual components of the experimental platform and its operating logic is carried out.

5.1 Functional structure

Considering the assigned tasks, the DSS can be divided into three main sub-systems: Motion moni-toring sub-system, Stress monitoring sub-system and Wave condition monitoring sub-system.

The main objective of the Motion monitoring sub-system is to evaluate the vessel motions on the seaway. It consists of the midship accelerometer, the midship angular rate sensor, and the inclinometer. The sensor locations are presented in Figure 5. The accelerometer measures the surge, sway, and heave accelerations. The angular rate sensor meas-ures the yaw angular velocity and the inclinometer measures the roll and pitch angles.

The purpose of the Stress monitoring sub-system is to evaluate the hull stress condition. It consists of four strain gauges that are oriented: Two strain gauges located starboard and portside of the mid-ship and two strain gauges located fore and aft of starboard. The strain gauges locations are also pre-sented in Figure 5.

The function of the Wave condition monitoring sub-system is to evaluate the wave spectrum. It con-sists of the wave height measurement sensor and the bow accelerometer. Their locations are shown in Figure 5. The bow mounted, down-looking, wave measurement sensor measures the relative wave height and the bow accelerometer compen-sates for the vessel motions.

5.2 Hardware structure

The hardware structure, shown in Figure 6, mainly consists of the real-time digital data acquisition sys-tem (DAQ), sensors, computers and power supply units. It is composed of the following units: Lap-top computer with external hard-drive, Desktop computer, GPS unit, sensors, Compact-RIO and EtherCAT, Ethernet switch and Power suppliers.

5.2.1 Laptop computerThe laptop computer acts as the main control equipment of the system. There are three software components that run on the laptop: User Interface, Main Program and Ship Operability Assessment Program. The first two are coded with LABVIEW, whereas the third is a standalone .net application developed in C#. An external hard-drive, with the purpose of saving real-time data for further analy-sis, and the GPS unit, are both connected directly to the laptop.

5.2.2 Desktop computerThe desktop computer runs the LABVIEW real-time operating system which enables high-speed calculation for Kalman filter based wave spec-trum estimation. The inputs to the wave spec-trum estimation program are the motion related sensor measurements (midship accelerometers,

Figure 5. Sensor locations.

Figure 6. Hardware structure.

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wave height sensor, inclinometer, and angular rate sensor), the vessel course and speed. The output of is the estimated wave spectrum including the wave direction, significant wave height and mean period. These quantities are then forwarded to the Ship Operability Estimation Program for further analysis.

5.2.3 GPS unitThe GPS NORTHSTAR Explorer 557 unit that has been installed on the experimental platform is presented in Figure 7. The unit comprises an exter-nal antenna and a RS-232 data communication cable. This unit is connected to the laptop through a combined USB/RS-232 communication port. The system parameters fetched by the GPS unit—vessel position, speed and course—are forwarded to the system by the laptop.

5.2.4 CompactRIO and EtherCATCompactRIO and EtherCAT units from NATIONAL INSTRUMENTS are used as the main data acquisition components for the DSS, and are pictured in Figures 8 and 9. The CompactRIO unit is located at midhsips and it is the central data acquisition hardware, whereas the EtherCAT unit is installed closer to the bow and acts as an exten-sion of the first.

Figure 7. GPS unit.

Figure 8. National instruments Compact-RIO unit.

Figure 9. National instruments EhterCAT unit.

5.2.5 Ethernet switchThe Unmanaged Ethernet Switch that enables communication between the CompactRIO unit, laptop, and desktop computer is presented in Figure 10. This unit incorporates an automated bandwidth management process that can secure the network from overloading among other errors. Furthermore, the unit is capable of online debug-ging and automatic recovering of IP addresses of other Ethernet linked units.

5.2.6 Other sensorsA picture of the compact system sensor configura-tion is presented in Figure 11. The CompactRIO is connected with several sensors: Angular rate sensor, accelerometer, inclinometer and four strain gauge sensors. The EtherCAT unit is con-nected with the wave height measurement sensor and its associated accelerometer, connection box and signal processors. The installation of the sig-nal processor with the EtherCAT unit is shown in Figure 12.

5.2.7 Strain gauge sensorsAs can be inferred from Figure 13, the sensor comprises a steel rod which is allowed to displace longitudinally at one end. The measurement of this displacement at the four distinct locations depicted in Figure 4 and consequent conversion to strain values, gives way to the assessment of the ship’s structural loads such as the vertical bending moment.

5.2.8 Wave height sensorThe bow mounted, down-looking, wave height measurement sensor is presented in Figure 14. This TSURUMI SEIKI wave height sensor consists of four components: sensor unit, accelerometer, con-nection box and single processing unit. The sensor component measures the relative height of the bow wave by resorting to a microwave Doppler method, while the accelerometer measures the vessel’s accel-eration so as to eliminate the relative motion effect.

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6 CONCLUSIONS

An prototype of an onboard decision support system for ship navigation under rough sea and weather conditions has been set up and installed on board of a Ro-Ro ship.

The system is currently collecting data as the ship operates, which will be used in its calibration and validation. Besides the motion measurement related instruments, strain gauges have also been installed. The time series of the values of the strains at these sensors locations, will serve as the basis

Figure 10. National instrument Ethernet switch.

Figure 11. The compact system with sensors.

Figure 12. Signal processor with EtherCAT unit.

Figure 13. Strain gauge sensor.

The connection box enables the linkage between both these components and the signal processor unit, from which the output is taken. The analogue output of the signal processor unit which is made available to the DSS is composed by the quanti-ties: ship displacement, relative wave height, wave height, significant wave height and average wave period.

5.2.9 Power suppliesThree power supply units are used to power the CompactRIO, EtherCAT, associated sensors and the GPS unit. One such power supply unit and its wiring connections are presented in Figure 15.

Figure 14. Wave height Senso.

Figure 15. Power supply unit with wiring connections.

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for the training of a neural network to be imple-mented, capable of quickly and accurately giving the expected loads given the present sea state and possible ship’s courses and speeds. The system has been successfully tested in terms of hardware and software integration. Another prototype installa-tion is being done on a container vessel, and the chronological steps will be the same as regarding the first ship.

ACKNOWLEDGEMENTS

This work is done within the project of “Handling Waves: Decision Support System for Ship Opera-tion in Rough Weather”, which is being funded by the European Commission, under contract TST5-CT-2006-031489.

The work of the first and second authors has been supported by research fellowship of the Portuguese Foundation for Science and Technol-ogy (Fundação para a Ciência e a Tecnologia) under contract SFRH/BD/46270/2008 and SFRH/BD/64242/2009, respectively.

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