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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]
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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
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(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.
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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
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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)
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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.
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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)
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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
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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
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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
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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
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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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