seawater cooling system reliability modeling for a safer...

International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:19 No:06 1

190206-3434-IJMME-IJENS © December 2019 IJENS I J E N S

Abstract— The internal combustion and friction of the

moving parts of ship machinery generate a great amount of

heat, leading to the increase of the running temperature, which

should be kept within the maker permitted thresholds. This is

ensured by the ship cooling system, which consists of two

independent systems, i.e. fresh water-cooling system and

seawater cooling system. The seawater cooling system plays a

vital role in the normal function of ship machinery. Its failure

leads generally to the overheating of the running equipment,

causing its breakdown and may lead to disastrous maritime

accidental events. Therefore, this system must be reliable and

continuously available to ensure the normal operation of the

engine room equipment. In this paper, we use the Bayesian

Network Analysis to evaluate the commonly used conventional

seawater cooling system to identify the weak system components

in order to enhance their reliability and to propose an improved

system with enhanced automation, that may be fitted onboard of

autonomous ship.

Index Term— Autonomous ship, Bayesian networks,

Conventional ship, Faults tree analysis, Fresh water system,

Reliability, Seawater system,

I. INTRODUCTION

The reliability of the shipboard equipment is one of the key

factors to ensure an efficient and sustainable marine

transportation. Unfortunately, the shipping industry has

experienced several maritime accidental events. From a total

of 1645 shipping accidental events analyzed during the

investigations, 57.8% were attributed to human erroneous

action, 25.5% were attributed to equipment failure. The ship

operation is considered as the main contributing factors with

70.1% of the total accident events; whereas 23.4% is

attributed to shore management [1].

The cooling system is one of the systems that has caused

many accidental events, resulting either in breakdown of

machinery, causing stoppage of Main Engine (ME) and

blackout or water ingress causing the damage of the engine

room equipment and ship sinking. On the 4th October 2009,

a blocked Seawater (SW) inlet filter at the Fresh Water (FW)

cooler had resulted in stoppage of the ME of the oil tanker

“Thames Fischer” and lead to a marine accident [2]. In July

2019, the Ship “Hassa E”, ME lubricating oil was

contaminated by SW, caused by the damage of SW system

piping, resulting in the damage of ME bearings. The ship

cooling system is mainly composed of two different and

independent systems, i.e. Seawater Cooling System (SWCS)

and Fresh Water-Cooling System (FWCS). There are three

main types of SWCS, i.e. SW circulation system, SW Central

Cooling System (SWCCS) and keel cooling system. For its

advantages, the SWCCS is widely used on board for

machinery cooling. This system is one of the vital systems on

board of ships. Hence, its reliability and availability are

important for the normal machinery running, to ensure a safe

operation of the ship. However, a safe operation of

autonomous ship (AS) will require a highly reliable SWCCS

capable to run autonomously without human intervention or

maintenance for a voyage of 28 days. Therefore, this study

focuses on the Conventional Ship’s (CS) SWCCS reliability

modeling, in order to improve it and to propose a SWCCS

that is reliable enough to be fitted on board of AS. For this,

many conventional ships have been visited, their SWCCS and

related documents were studied.

In our previous work, we studied the SWCC system in

term of reliability, failure rate, Mean Down Time (MDT),

Mean Time To Failure (MTTF), using Faults Tree Analysis

(FTA) and Failure Modes and Effect Analysis (FMEA)

methodologies [3]. However, it is still not good enough to rely

on FTA for better failure detection and prediction. Because,

in real state, it requires a certain causal relationship among

events in the tree structure. Thus, in this work, the Bayesian

Networks (BN) is used and which is a more suitable methods

to present the uncertainty and correlation of variables to

assess the conventional ships SWCCS reliability in order to

identify the system weak points for their improvement and

also to give valuable suggestions to shipowners regarding

failure detection, system maintenance policy and the proposal

of highly reliable system’s concept with an enhanced

automation that may be installed onboard of AS.

The rest of the paper is structured as follows. In section II,

an overview of related work is given. In section III, the

different types of SWCS are presented. In section IV, the

analysis’s approach methodology and materials are explained.

In section V, the result of the analysis is presented and

discussed. The system weak points were depicted. An

Seawater Cooling System Reliability Modeling

for a Safer Autonomous Ship

A. AIT ALLAL 1 a, A. KAMIL 2, Y. MELHAOUI 2, K. MANSOURI 1, M. YOUSSFI1 1Laboratory: Signals, Distributed Systems and Artificial Intelligence (SSDIA)

ENSET Mohammedia, Hassan II University of Casablanca, Morocco 2Laboratory LAMS, Faculty of Sciences Ben M’sik, Hassan II University of Casablanca, Morocco

a Corresponding author: [email protected]



improved system is proposed with the possibility to be

remotely controlled, followed by a benchmarking between

conventional system and improved system. In section VI, the

paper is concluded, giving a summarization of the obtained

result and its importance for the designer at the design stage

and for the shipowners, to implement an efficient

maintenance policy for a safe operation of CS and AS at the

operational stage.

II. LITERATURE REVIEW

In the past decades, the SWCS has been subject to many

pertinent studies. Mainly, research has been emphasized on

system reliability, energy optimization and environmental

impact. Pugh et al. (2003) developed a practical user guide

for the current state of knowledge relating to fouling in

cooling systems using seawater. Its objective is to provide the

designer and the operator of both onshore and offshore

equipment with a practical source of guidance on the

occurrence, the mechanisms and the mitigation of seawater

fouling in these systems [4]. Kocak et al. (2017) optimized

the SWCS’s energy by fitting pumps variable speed driver to

adjust the SW flow. The energy-saved was calculated for

different sea water temperatures during the ship sea passage,

and assessment of its environmental impact [5]. Theotokatos

et al. (2016) investigated two cases. First, a conventional case

of controlling the sea water and fresh water temperatures by

using three-way valves and second, a more sophisticated case

of installing variable speed motors for driving the system

pumps. The obtained results are compared in terms of annual

power consumption leading to conclusions about the system

performance [6]. Ait Allal et al. (2017) computed the

SWCCS sea-chest maintenance task and analyzed it from

human perspective. The human error probability was

quantified, using human reliability analysis (HRA) and the

technique for human error rate prediction (THERP). Based on

this analysis, error barriers and error recovery mechanisms

are proposed to prevent its consequences [7]. Fu et al. (2015),

gave an efficient solution to prevent and protect the condenser

seawater cooling system against corrosion and fouling, by

using suitable materials, biocide treatment and efficient

cleaning [8]. Boroken (2016) studied the ship systems

reliability in order to propose solutions to improve them [9].

Durmusoglu et al. (2015) calculated the energy consumption

and energy-saving of a pumping system in different

maneuvering situations. Also, the economic gain and

efficiency increases were discussed [10]. Su et al. (2014)

proposed an energy saving method by variable frequency

control of sea water cooling pump driver that is affected by

the sea trading area [11]. Kleinmann et al. (2012) discussed

in detail the model-based diagnosis approach and fuzzy logic

approach for the advanced diagnosis of industrial pumps

systems [12]. Handan et al. (2011) presented a model of a

reliability analysis in the system dynamics (SD) simulation in

order to predict and prevent potential failure of maintainable

items of ship machinery components, and to priorities the risk

and minimize the maintenance cost to obtain a reliable ship

machinery component [13]. Zhai et al. (2013) discussed how

to establish and construct a multi-state system model and

proposed a method for reliability modelling and assessment of

this multi-system based on Bayesian Network (BN). This

approach permits a qualitative and quantitative analysis of the

multi-state system reliability, identifies the weak links of the

system, and achieves assessment of system reliability [14].

Zhou (2014) summarized research on approaches for

Bayesian Network learning and inference. He developed two

groups of models with multi-states nodes for constant and

continuous time to apply and contrast Bayesian networks with

classical fault tree analysis method were developed [15]. Qiu

et al. (2016) proposed an optimal method for the allocation of

critical system redundancy to maximize the system reliability

[16]. Canbulat et al. (2018) used the probabilistic Bayesian

Belief Networks to optimize both port and ship operations.

The study aims to keep cost efficiency, maximize energy

efficiency, and reduce shipping and port operations gas

emissions [17]. Nabdi et al. (2017) presented the Bayesian

Networks as a modeling tool for the study of wind turbine in

order to construct a decision choice between two concepts of

turbine i.e. direct and indirect [18].

However, in this work, we study the SWCCS itself by

modeling its reliability by using BN analysis methodology to

reveal its weak points and rooms of improvement, in order to

propose a highly reliable system that is capable to function

without human intervention for at least 28 days, time to reach

the maintenance facilities. Also, to help designers to upgrade

the functionality and robustness of the system and to support

the shipowners and crew to implement an efficient

maintenance policy to ensure its continuous availability.

III. DIFFERENT TYPES OF SHIP SEA COOLING SYSTEMS

A. Seawater Circulation Cooling System

In this system (Fig. 1), the SW is used directly as cooling

media in the machinery heat exchangers. the SCPP1 or

SCPP2 sucks SW from sea trough the sea chest strainer and

pump it out directly in the main engine (ME) lubricating oil

cooler, ME jacket water cooler, ME charge air cooler, Boiler

condenser, air conditioning plant, and other auxiliaries, to

absorb the machinery undesirable produced heat. This results

in an accelerated corrosion of heat exchangers, piping and

other parts in contact with seawater. More than that, the

interval of cleaning of heat exchangers, is reduced due to the

accumulation of dirt and solid matters in the coil, resulting in

the decrease of heat exchange efficiency. However, the

installation of this system is cost-effective, but its life-cycle

maintenance is costly.

B. Keel Cooling System

In the keel cooling system, the FW cooler, called also the

box coolers are placed outside the ship hull into the sea chests



(Fig. 2). Each equipment has an individual cooling system. Only the ME cooling system is presented in the figure. The

Fig. 1. Seawater circulation cooling system

Fig. 2. Keel cooling system

cooling FW circulates in closed loops through ME coolers

such as, ME cooler, ME jacket water cooler, ME charge air

cooler and ME piston cooling. The continuous circulation of

cooling FW is assured by two Low Temperature FW Pumps

LTFWPP1 and LTFWPP2. One of the pumps is enough for

the water circulation, the other one is kept in standby status.

This means that no SW cooling circulation inside the hull,

which results in limitation of SW piping, usually subject to

aggressive corrosion, leading to piping damage and water-

ingress. Also, this design permits the use of cheaper piping

and valves materials, making of it a simple and a cost-

effective system.

C. Conventional Seawater Central Cooling System

The SW Central Cooling System (SWCCS) (Fig. 3),

(Fig. 4) is an open loop system. The table I summarizes the

description of the codes used in the system’s drawing. The

High Sea- Chest (HSC) and the Low Sea-Chest (LSC) grids

are placed respectively on both sides of the ship and must be

kept continuously submerged below seawater line to avoid

air’s suction. This ensures the permanent fill up by gravity of

Low Strainer (LSTR), High Strainer (HSTR) and SW cross-

manifold. The SW Cooling Pump 1 (SCPP1) or SCPP2

depending on which one is in use, sucks from the SW cross-

manifold and pumps it out into the system. The pumped water

passes through the in-service Low Temperature FW cooler

(LTFWCL1) or LTFWC2 and absorbs the undesirable heat

from the Low Temperature FW (LTFW) system and then is

thrown overboard back to the sea. The LTFW system, which

is not subject of our study, works in closed loop. Once the

LTFW is cooled in the LTFWCL1 or LTFWCL2, then passes

through machinery and absorbs the undesirable heat, to keep

it within normal running temperature thresholds and then

back to the LTFWCL1 or LTFWCL2 to be cooled down and

so on. At its passage through the system, the SW is filtered,

to retain the foreign matters. First it is filtered through LSTR

or HSTR, then through the inlet pumps strainers SCPP1STR

or SCPP2STR and at the last phase through coolers Internal

trainers ISTR1 or ISTR2. In dirty SW, i.e. in port, or in the

river, it happens that the sea chests grids might be clogged.



Fig. 3. Conventional seawater central cooling system

TABLE I

SEAWATER COOLING SYSTEM COMPONENTS CODES AND DESCRIPTION

Fig. 4. Conventional seawater central cooling system components view

The back-flushing system might resolve the problem

temporarily of sea-chest clogging, by pushing back to the sea

the dirt and foreign matters, waiting for a final cleaning by a

diver or when the ship is at the dock. Various valves are fitted

at the inlet and outlet of each system’s component, to isolate

it in case of routine maintenance or damage. In the present

work, we take the SWCCS as a case study, due to its

common use on board of CS, its advantages in term of

limitation of SW piping and its maintenance cost-

effectiveness.

For an autonomous ship, this system must function

without failure at least for a sailing period of 672 h, which is

equivalent to 28 days without human intervention, time to

arrive to the port, where a repair team might intervene. The

Code Description Code Description

EJPP Ejector pump LTC1Vo LTFWCL1 outlet valve

EJPPVi Ejector pump inlet valve LTC1BFVi LTFWCL1 back flushing inlet valve

EJPPVo EJPP outlet valve LTC1BFVo LTFWCL1 Back flushing outlet valve

EJPPSTR EJPP strainer LTFWCL2 Low temperature fresh water cooler 2

GSPP General service pump LTC2Vi LTFWCL2 inlet valve

GSPPVi GSPP inlet valve LTC2Vo LTFWCL2 outlet valve

GSPPVo GSPP outlet valve LTC2BFVi LTFWCL2 back flushing inlet valve

GSPPSTR GSPP strainer LTC2BFVo LTFWCL2 back flush outlet valve

HSCS High sea chest starboard OBNRV Non-return over board valve

HSCSVi HSCS inlet valve OBV Over board valve

HSCSVo HSCS outlet valve SCPP1 Sea cooling pump 1

HSCSBFLV HSCS back flush valve SCPP1Vi SCPP1 inlet valve

HSCP High sea chest port side SCPP1Vo SCPP1 outlet valve

HSCPVi HSCP inlet valve SCPP1STR SCPP1 strainer

HSCPVo HSCP outlet valve LSCSBFLV LSCS back flushing valve

HSCPBFLV HSCP back flushing valve LTFWCL1 Low temperature fresh water cooler 1

LSCP Low sea chest port side LTC1Vi LTFWCL1 inlet valve

LSCPVi LSCP inlet valve SCPP2 Sea cooling pump 2

LSCPVo LSCP outlet valve SCPP2Vi SCPP2 inlet valve

LSCPBFLV LSCP back flush valve SCPP2Vo SCPP2 Outlet valve

LSCS Low sea chest starboard SCPP2STR SCPP2 strainer

LSCSVi LSCS inlet valve SW Sea water

LSCSVo LSCS outlet valve V1-V2-V3-V4-V5 Interconnection valve



Fig. 5. Faults tree analysis representation shapes

decision of fixing 28 days as interval between human

intervention is based on in-situ study that shows that

generally

the cleaning of LSTR and HSTR is carried out monthly, and

that the AS may be subject to delay of arrival to port, due

either to weather condition or to port congestion. Also, in

some ports there is no maintenance team to carry out the

scheduled repair jobs. Therefore, it is important to identify its

failures mode, root causes, and potential risks, for an

adequate improvement of the system. In this work, the system

reliability modeling is supported by Bayesian Network

analysis methodology.

I. MATERIALS AND METHODOLOGY

The study is based on visit of several ships’ types, i.e.

container ship, general cargo ship, and bulk carrier ship. We

studied the relevant documents, such as system drawing,

engine logbook, planned maintenance system. Also, we

interviewed the crew for their feedback and experienced

failures cases. In the previous work, we used Fault Tree

Analysis (FTA) to analyze the system reliability. Whereas, in

the present work, we use Network Analysis (BNA)

methodology to assess the reliability of the SWCCS.

A. Faults Tree Analysis Methodology

The FTA is a backward graphical representation, where

the failure event of interest called “top event” is selected and

the

possible root causes called “basic fault events” are traced from

up to down. The FTA is illustrated by using logic gates, i.e.

“AND gate” and “OR gate” (Fig. 5). This method is widely

used to analyze the failure roots, and to evaluate the ship

equipment reliability in shipping industry.

SWCC System Faults Tree Analysis

In our case, we divided the system into subsystems, i.e.

pumping system, coolers system, sea chests system, piping

and human error . The Fig. 6 presents the graphic of SWCC

system FTA. In reality, SWCCS components have a

continuous time failure due to various constraints and fatigue,

resulting in damage and downtime.

For this FTA we made the following assumptions:

- The failure of each component is independent.

- There is no correlation between the paralleled events.

- The failure probabilities for the roots are related to the

working time t, given by the function (1),

Where λi is component’s failure rate, t is the system running

time in h (hour).

The table II summarizes the failure rate for each system

component. These values are based on the collected data from

the following references, [19]-[20]-[21] and on our

experience and expertise.

A discretization of time is necessary to make the system

reliability inference. We set Δ𝑡= 96 hours as a time interval,

which is equivalent to 4 days. Also, we set the system

running time to 28 days, which gives 7 iterations in the fault

tree. and 7 different failure probabilities for each continuous

node. The failure, probabilities for Human supposed constant,

while failure probabilities for other roots increase over time as

shown in Fig. 7, leading to an increase of SWCCS failure

probability as illustrated by Fig. 8.

(1)



Fig. 6. Conventional Seawater central cooling system fault tree analysis

TABLE II

SYSTEM COMPONENTS FAILURE RATES

Designation Failure rate (MH)

Sea cooling pump λpp = 51,66 x 10-6

Coupling λcp = 25 x 10-6

Motor λm = 16,25 x 10-6

Sensors λss = 8,96 x 10-6

piping λpi = 7,93 x 10-6

Valve λv = 7,68 x 10-6

LT cooler λcl = 26,85 x 10-6

Sea chest λsc = 700 x 10-6

Human λH = 1000 x 10-6

Fig. 7. Failure probability for each root

Fig. 8. SWCCS failure probability

Limitation of Faults Tree Analysis Methodology

The continuous time fault tree approach is based on

classical probability theory which use simple Boolean

relationships. It can present the change of component failures

and system failures over time. It proves a limited information

about the system reliability. Even though the failure rate of

the top event can be calculated and some events with more

critical influence on the system reliability can be identified, it

is still not good enough to rely on FTA for better failure

detection and prediction. Because it requires a certain causal

relationship among events in the tree structure. However, in

real case, the causality between events is uncertain. therefore,

a conditional probability and a bidirectional inference about

the system reliability are more suitable methods to present the

uncertainty and correlation of variables. By Using a dynamic

BN model of the fault tree, which we introduced in the

following section, permits to avoid this significant limitation

of the FTA approach and give a better representation of the

reliability of the whole system.



Fig. 9. Directed acyclic graph description

TABLE III

SYSTEM COMPONENTS BAYESIAN NETWORKS USED CODES

Bayesian

Networks

codes

Description Bayesian

Networks

Codes

Description

Pp Pump PS Pumping system

Cp Coupling CS Cooling system

M Motor HSC High sea chest system

P Pumping LSC Low sea chest system

SS Sensors H Human

Pi Piping PB Pumping block

Vi Valve inlet CB Cooling Block

Vo Valve outlet SCB Sea chest Block

Cl Cooling S System

B. Bayesian Networks Methodology

Bayesian inference and BN approach are based on the

Bayes’ theorem, which was initially developed in the 1760s

and which updates the probabilities based on new

information. A Bayes’ formula was developed then by some

statisticians, including Pierre-Simon La Place, as a systematic

inference and decision-making method. In 1988, Judea Pearl

proposed the BN’s which is the current methodology that uses

the prior statistics information in the statistics [22]. This

method has successfully found its application in various

science and industrial fields.

Bayes' theorem is given by the equation (2),

where “A” and “B” are events and P(B) ≠ 0;

- P(A) is the probability of event “A” happening;

- P(B) is the probability of event “B” happening;

- P(B|A) is the conditional probability of event “B” given the

probability of a given event A;

- P(A|B) is the conditional probability of event “A”

happening given event “B” happening.

William M. Bolstad suggest a general form outlined in

equation (3),

Where P(A) ≥ 0, P(B) ≥ 0 and P(Bi) consists of mutually

exclusive events within the universe S.

Bayesian Network (BN), also known as Belief Network, is

a probabilistic graphical model that represents the knowledge

about an uncertain domain via a Directed Acyclic Graph

(DAG) [23]. In the DAG, each node in the graph represents a

Fig. 10. Conventional SWCCS Bayesian Network structure

(2)

(3)



TABLE IV

MARGINAL PROBABILITY DISTRIBUTION FOR EACH ROOT

Node P(Pp) P(Cp) P(M) P(Ss) P(Pi) P(V) P(Cl) P(Sc)

1 1 – 𝑒−𝜆Pp

𝑡 1 − 𝑒−𝜆Cp

𝑡 1 − 𝑒−𝜆M𝑡 1 − 𝑒−𝜆

Ss𝑡 1 − 𝑒−𝜆

Pi𝑡 1 − 𝑒−𝜆

v𝑡 1 − 𝑒−𝜆

Cl𝑡 1 − 𝑒−𝜆

Sc𝑡

2 𝑒−𝜆Pp

𝑡 𝑒−𝜆Cp

𝑡 𝑒−𝜆M𝑡 𝑒−𝜆

Ss𝑡 𝑒−𝜆

Pi𝑡 𝑒−𝜆

v𝑡 𝑒−𝜆

Cl𝑡 𝑒−𝜆

Sc𝑡

random variable and is represented by a circle labelled by the

variable name as per table III, while the edges between the

nodes represent probabilistic dependencies among the

corresponding random variables and are illustrated by arrows

linking nodes. For example, if an edge is from node X to node

Y, as shown in Fig. 9, then X is Y’s parent variable. To

construct a BN, both DAG structure and the probability

parameters must be defined.

First, the topological structure of SWCCS fault tree is

transformed to the network structure of BN’s which presented

by the Fig. 10. Second, for each root node, all its possible

states and its probabilities are presented by a Marginal

Probability Distribution (MPD). For every other node in the

BN’s model, a Conditional Probability Distribution (CPD) is

used to describe its probability distribution giving the states of

the parent nodes. The nodes MPDs and CPDs in the SWCCS

Bayesian network model are inferred from the fault tree, as

shown respectively in table IV and table V, where each node

has two states. When a node is equal to 1, then it means the

event fails; when it is equal to 2, then this part of the system

is still functional. The CPDs for the rest of the nodes are

calculated in the same way as the CPDs of the node P1. When

a node is equal to 1, then it means the event fails; when it is

equal to 2, then this part of the system is still functional. The

CPDs for the rest of the nodes are calculated in the same way

as the CPDs of the node P1. A probability like P(P1=1) is

called a prior probability because. It is the probability of the

event before knowing any information about other events. A

probability like P(P1=1|Xi=1), in which Xi could be one of

the parents of P1, called a posterior probability because it

represents the probability of an event depending on another

TABLE V

CONDITIONAL PROBABILITY DISTRIBUTIONS FOR THE NODE P1

Pp1 Cp1 M1 P(P1=1| Pp1, Cp1, M1)

1 1 1 1

1 2 1 1

1 2 2 1

1 1 2 1

2 1 1 1

2 2 1 1

2 1 2 1

2 2 2 0

event prior probability updated information. These

probabilities are calculated by the Bayes’ formula. Given that

an event could fail or operate, its conditional probability of

Fig. 11. SWCCS failure probabilities distribution

dependencies is calculated, using the junction tree engine as

an inference engine in Matlab.

BN’s is considered as a decision-making model that leads

to multivariate knowledge. Explicitly, the conditional

probabilities, based on both forward and backward

information, provide a targeted analysis on system reliability

and give valuable suggestions for failure detection and system

maintenance policy.

I. RESULT AND DISCUSSION

A. Conventional SWCCS Bayesian Networks Analysis

Results

Using the same failure rates as in the FTA, the failure

probability of the SWCCS is still the same as the fault tree

approach result, as shown in Fig. 11. This indicates that

Bayesian Networks can perform the functions of the FTA.

The conditional failure probabilities distribution of each

component over time, given that system is failed provides a

knowledge about the influences of each component failure on

the global system failure, as illustrated in Fig. 12. When the



Fig. 12. Conditional failure probability of each component given the system

failed

system is failed, the failure probabilities of the LSC and HSC

and SCPP’s are significantly increasing with time, while the

failure rate of other components slightly increase over the

system running time. Considering that sea chests and sea

cooling pumps have the highest failure rate among all the

components in this SWCCS. they have a significant

contribution in the whole system reliability. Any failure of

these two sub-systems will lead to the whole system failure.

Conditional probability distribution of the leaf node S, given

that each component of the SWCCS is failed, is illustrated by

Fig. 13. It determines exactly which component failure leads

to the system failure. The conditional probability distribution

of the system, given that one of the components Pi5, PB, CB,

and SCB is failed, is equal to 1. This shows that any failure of

components Pi5, PB, CB, SCB will cause the system’s failure.

Fig. 13. Conditional failure probability of the leaf node S given that each

component of the SWCCS failed

B. Conventional SWCCS Reliability Weak Points

Based on analysis results and as commonly designed, the

SWCCS presents several weak points, i.e. LSC, HSC, piping

arrangement, SCPP’s and human erroneous action. These

weak points are the root cause of several marine accidental

events. According to the “Annual Overview of Maritime

Casualties and Accidents 2018” statistics, from 2011 to 2017,

around 25.5% of maritime accidental events were attributed

to equipment failure and from a total of 1645 accidental

events analyzed during investigation, 57.8% were attributed

to human erroneous action [1]. On the 4th October 2009, a

blocked SW inlet filter at the FW cooler had resulted in

stoppage of the ME of the oil tanker “Thames Fischer” and

lead to a marine accident. The primarily investigation of the

UK’s Marine Accident Investigation Branch found that the

shipping company owning the vessel had a history of failures,

related to the SW cooling system [2]. In 2017, A SW cooling

pipe failed during planned maintenance onboard of a vessel

in “cold” lay-up, causing SW ingress. Although there was no

damage, this near miss had the potential for major equipment

damage or loss of the vessel [24]. The other recent damage

happened in July 2019, on board of the ship “Hassa E”, where

the ingression of SW, caused by the damage of the SWCCS

piping had increased the bilge water level, causing the

intrusion of



Fig. 14. Autonomous ship improved sea water central cooling system

SW in the lubricating oil (LO) sump tank, through the rubber

membrane of the crankcase LO outlet. Consequently, the LO

was contaminated by SW and turned to mayonnaise status.

This led to the stoppage of the ME and damage of its

bearings, i.e. crossheads bearings, crankpin bearings, main

bearings and turbocharging bearings. Following a deep

damage investigation, it was found that the oil contamination

by SW was the key factor that had accelerated the apparition

of the damage. This damage repair had caused an off hire of

the vessel for almost five weeks in addition to the repair cost

resulting in a shortfall estimated at €420.000 and a maker

service specialist support estimated at €70.000 . Thus, the

SWCCS must be improved at the design stage and later on,

an efficient maintenance policy must be implemented at the

operational stage. Therefore, in the present work, we propose

an improved model of SWCCS, which is illustrated in Fig.

13, that may be installed either on board of autonomous ship

or on board of conventional ship.

C. Proposal of an Improved SWCCS

The improved system (Fig. 14) functions in the same way

as the conventional model. The difference lies in the

redundancy enhancement of several components, i.e. SCPP’s,

LSC, HSC and re-configuration of valves and piping

arrangement. To enhance its automation, various monitoring

sensors were fitted, such as pressure sensors, pressure

differential sensors, temperature sensors, SW leak sensors,

vibration sensors, noise sensors, valves position sensors. This

re-configuration of the system in terms of redundancy and

automation contributes positively in the reduction of human

intervention and enhancement of relaibility and safety on

board of the ship, to reduce the maritime accidental event and

to ensure a sustainable maritime industry. The system failure

probability results in a significant reduction which make the

system’s reliability value closer to 1 as shown in Fig. 15.

Fig. 15. Failure probabilities distribution of the improved SWCCS

D. SWCCS Automation and Remote-Control Concept

The fitted SWCCS on board of AS must work

autonomously without human intervention. The SCC team

may intervene remotely either to restore the normal situation

after failure of the system or to modify its operation data. The

“SWCCS automation system” manages the operation of

system depending on the inputs, which consists of signals

received from different system sensors, such as pressure

sensors, pressure differential sensors, temperature sensors,

SW leakage sensors, vibration sensors, valves status sensors,

noise sensor and from other associated control sensors placed

at different points of the system, depending on the level of

automation and number of components. Whereas, the outputs

consist of orders signals that are given to control different

system components, such as open or close valves, changeover

of sea chests, start or stop of SWCPP’s, rise alarm, in case a

fault is detected. When a fault is detected, the system tries to

solve it autonomously at the “SWCCS automation system”

level. If it is solved, the system is restored to its normal

operation. If it is not solved at this level, the “Autonomous

engine room monitoring and



Fig. 16. SWCCS autonomous operation and remote-control concept

control” system tries to solve it. This last system monitors the

autonomous operation of all equipment installed in the engine

room of the ship. In case all above cited systems fail to solve

the problem, using the ship embarked technologies and

algorithms, the ship send alert to SCC, to take remotely the

control and intervene to solve the problem. Supposing that

the ship cannot get in contact with the SCC, due to a

communication interruption at that moment, the ship goes to

“Fail to safe” status, using a predefined emergency plans that

are initially programmed by the SCC to avoid accident and

damage to the ship and to infrastructures in its vicinity,

waiting for the communication to be re-established (Fig. 16).

E. Benchmarking of the Conventional System and Improved

System

The proposed SWCC system reliability assessment result

is obtained by adopting the same calculation approach used

for the conventional system. The Fig. 17 depicts the

benchmarking

Fig. 17. Failure probability distribution of both conventional system and

improved system

of the conventional system and the proposed system in terms

of reliability. It shows an improvement of system reliability

and decrease of failure rate. This improvement is obtained by

redundancy enhancement of components, piping

reconfiguration and elimination of human on board. With the

autonomous ship concept, the human error probability will be

eliminated because there will be no crew on board, resulting

in a significant reduction which make the system’s reliability

value closer to 1 as shown in Fig. 15.

I. CONCLUSION

The systems which may be installed on board of AS, must

be highly reliable and continuously available. The SWCCS is

one of the vital systems that must be designed with reliable

components and enhanced redundancy. In this work, a fault

tree based dynamic BN is applied for SWCCS modeling and

assessment. This analysis results in a quantitative and

qualitative assessment, identifying the system weak points in

order to propose improvement, either at the design stage or at

the operational stage. BN is a powerful methodology for

reasoning under uncertainty and making better inference by

taking the advantage of using more information and

discretizing some variables. The obtained result shows the

BN’s straightforward side to reassess continuous and dynamic

system reliability, permitting the system failure detection and

prediction to implement an efficient planned maintenance

strategy. The SWCCS analysis has demonstrated the

vulnerability of the filtering and pumping sub-systems.

Particular attention must be paid to these sub-systems which

require improvement of the reliability of their components

and enhancement of their redundancy. The enhanced

automation of the system and the possibility to control it



either autonomously depending on trading area and energy

efficiency policy or remotely by direct intervention of SCC

will reduce significantly the human erroneous action and rend

the ship robust and safer.

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