This research uses machine learning on sensor data from ships to predict failures of components and their remaining useful life. Interviews with marine experts identified significant maintenance items to prioritize for ship supply chains. The results were analyzed to provide recommendations to a government company on implementing predictive analytics and supply chain strategies for ship maintenance in Malaysia.

1. By KEE Kok Yew, Geoffrey
Summary: This research is performed to use machine learning approaches using identified sensors data on the ship
to predict the next component’s failure measuring its Remaining Useful Life (RUL) for an effective supply chains of
operation maintenance at a strategic decision-making level. By interviewing subject matter experts from marine
engineers and a government linked company (GLC) in Malaysia, this research provides an initial framework of
prioritizing significant maintenance items (MSI) for ship supply chains. The results obtained from the methodology
have been analyzed to provide recommendations to the GLC about the importance and implementation along with
the ownership of the predictive analytics and its supply chain strategies for ship maintenance in Malaysia
Kok Yew Kee received his MBA in Strategic Management & Change from the University of
Strathclyde, Glasgow and a Bachelor of Commerce in Accounting & Finance from the Murdoch
University, Western Australia. While undertaking his next Master studies in Supply Chain
Management from Malaysia Institute of Supply Chain Innovation(MISI), he is a Data Scientist at
MIMOS Berhad, a National IT Research & Development company in Kuala Lumpur, Malaysia.
Introduction
The project was originated from a government linked
company that manages maintenance for the naval
forces in Malaysia. As part of the requirement for
transformation within the fleet types and management
which is to cut down on operational costs and reduce
dependency on foreign supply on maintenance and
parts requirements , an Integrated Logistics and
Information System (ILIS) was implemented for the
objective of consolidating various independent system
i.e. Finance, Human Resources, Operations, Strategic
and etc., into a single unified system from ship to base
and the Headquarter on shore. In addition, ILIS adopts
4th
Industrial Revolution (4IR), or Industry4WRD, a
national policy that focuses on the following pillars:
i. Internet of Things (IoT), where by ship
sensor data is being monitored and
analysed
ii. Cybersecurity, which is designed to
ensure safety and confidentiality of data
being transferred over the network
iii. Cloud Infrastructure, which provides
storages for Big Data Warehouse
iv. Big Data Analytics (BDA) and Artificial
Intelligence (AI), where various data, at
rest and on the move, being
continuously analysed to provide
actionable insights
v. Augmented Reality (AR), by enabling
effective AR-assisted training and
maintenance activities
Toward Predictive Maintenance for Marine Sector in Malaysia
KEY INSIGHTS
 The availability of Big Data and the use of Predictive analytics, can ensure that early potential
failures on critical components be identified, which supports an effective maintenance strategy in
the shipping industry
 Availability of Sensors data are important, followed by Maintenance Significant items (MSI), has
been identified as areas of concern to focus upon for predictive maintenance in the ship system
 The proposed framework and methodology can be used to identify important components for any
equipment which is critical in constructing effective supply chain strategy for repair and
maintenance.

2. The main objective of the system is to enable the ships
to achieve highest level of readiness while optimizing
its cost of maintenance in the long run. One way to
improve operational availability are as follows:-
 To prioritized preventive maintenance
items “ just in time” to avoid the cost
and,
 ‘down time’ of corrective maintenance
(breakdowns)
Literature Review
According to (Pintelon & Parodi-Herz, 2008), defines
maintenance as the “set of activities required to keep
physical assets in the desired operating condition or to
restore them to this condition”. In details, the following
key aspects of maintenance can be described:-
1. Maintenance requires a certain kind of
activities;
2. The particular asset’s performance are
well defined, measured and influenced
by certain conditions;
3. The maintenance process is normally
associated with physical assets;
4. Two goals of maintenance; there are to
maintain a certain condition or restore
the asset back to original condition
Many literatures clearly shows the distinction on the
maintenance policies. These includes:-
1. Corrective (unplanned) maintenance
2. Preventive(planned) maintenance
3. Predictive(ConditionBased)
maintenance
Figure 2, shows the distinction between the three
maintenance options (White 1979)
(White, 1979) defines Preventive (Planned)
Maintenance periodically as “Maintenance scheduling
or planning embraces all activities necessary to plan,
control, and record all work in connection with keeping
an installation to an acceptable standard”. According to
(Nowlan & H.F., 1978), there are four tasks to protect
the reliability and safety of a system as follows:-
1. Inspection of a component to detect
failure
2. Failure detection
3. Reworking and discarding of a
component before its maximum age
4. Inspecting an item to assess unseen
failures
In (Raptodimos, Lazakis, Theotokatos, Varelas, &
Drikos, January 2016), there has been some proven
concept in smart ships where they aim in integrating
robotic platforms, structural and machinery reliability
tools in order to enhance ship inspection, maintenance,
safety and performance. In the article, it was mentioned
that understanding of equipment failure behavior are
important for record and evaluation given different
measureable parameters. In (Sullivan, Pugh, Melendez,
& Hunt, 2010), it was further discussed on how various
condition monitoring technologies and techniques such
as lubricant/fuel, wear particle, bearing temperature,
infrared thermography and motor current signature
analysis are used for condition monitoring in
developing a CBM strategy for equipment. Predictive
analytics is an area of data mining that deals with
extracting information from data and uses it to predict
trends and behavior patterns. According to (Murphy,
2012) (Nyce & Cpcu, 2007), mentioned that usually the
unknown event of interest is in the future, but
predictive analytics can be applied to systems with any
type of unknown relationship among their components
whether it exists in the past, present or future.
According to (Raptodimos, Lazakis, Theotokatos,
Varelas, & Drikos, January 2016), Table 2, were
tabulated as all possible systems to be monitored and
numbers of sensors required per each of them as shown
below.
System Type of Sensors Number of
Sensors
Main Engine Temperature,
pressure,
vibration,
clearances,
deflection
12
Turbocharger Temperature,
pressure
2-4
Steering Gear Flow rate,
pressure,
electrical
2-4
Pumps Vibration,
electrical, flow
rate, pressure
2 per pump
Table 2. Numbers of Sensors per system
Maintenance
Planned
Maintenance
Preventive
Maintenance
Condition
Based
Maintenance
Periodic
Maintenance
Corrective
Maintenance
Unplanned
Maintenance

3. Methodology
Main engine as one of the main components in the
Main Propulsion system is the area of focus of the study
to be reported in this paper. The Figure 7 below refers,
Figure 8. A Typical Diesel Engine of a Ship
The main scope of this project aims to study how
predictive analytics can be used for machinery
performance and its maintenance, to enable operational
availability and system effectiveness. In an article from
(Simoes, Viegas, Farinha, & Fonseca, January 2017),
the detective maintenance or maintenance intelligent
system brings together the prediction and prevention
options. The article further reiterates that an intelligent
maintenance management system must provide the
following research questions which can be adopted in
our paper:-
i. Where the dysfunction symptom is
located (component or sub-system)?
ii. What is the primary cause of the
dysfunction symptom?
iii. What time does mediate until the
occurrence of critical states?
iv. What are the consequences of the
dysfunction?
v. What is the recommended maintenance
action?
In order to fulfill the above research questions,
observations through monitoring the main engine’s
physical parameters such as pressure and temperature
were performed and data were collected.
As large volume data were collected from the ship
system as well as documentation on-board were copied
for streamlining purpose so that a long list of row data
is cleaned and prepared for being analyzed by models,
which often is the 1st
step in modelling development
stage. Kindly refer to Figure 9 below,
Figure 9. Modelling Approach – Main engine
In this paper, data collection can be done quantitatively
or qualitatively, or through a mix between the two
(Saunders, Lewis, & Thornbill, 2007). The following
approaches shall be conducted in this research project
as follows:-
i. Case Studies, Interview and Site Visits
ii. Raw data collection at the ship
iii. Technical and Operation Manuals
iv. Planned Maintenance Schedules
v. Mathematical modelling, Simulation
and Statistical Analysis
vi. Big Data Analytics and Machine
Learning algorithm using Open Source
Software e.g. Python or R programming
language
Data is retrieved based on the conditioning oil system
in the lubrication system of the main engine. There
are three main features in the conditioning oil system
which are engine oil pump, thermostat and engine oil
filters. The function of engine oil pump is to circulate
engine oil under pressure to the rotating parts. The
thermostat is used to measure pressurized oil to heat
exchanger for the purpose of cooling and the main
features of engine oil pump has centrifugal filter and
also to control oil in order to deliver hydraulic
pressure. All these functions are essential to main
engine reliability analysis in this paper.

4. Table 4. Data collected based on Main Engine
Parameters on the ship
The raw data information were collected for over a
year from 2017 - 2018, however, we utilize solely
based on the recent four months operation of the main
engine component inside the main propulsion system
of the ship, which includes 850 rows of data or 12847
data recorded by calibrated sensors every hour, where
actual failure events were registered. The recorded
data has been cleaned and preprocessed before
deploying modelling techniques.
Predictive analytic is an area of data mining that deals
with extracting information from data and uses it to
predict trends and behavior patterns. In our research
paper, we are keen to predict the next failure of
important sub-components within the main engine, by
understanding the unknown parameters or
relationship among the other components whether it
exists in the past, present or future. As Artificial
Neural Network (ANN) discussed above is more of a
non-parametric approach that makes fewer
assumptions, the result outcome may not be sensible
to Subject Matter Expert (SMEs) in marine
engineering field. Hence, we had to conduct an in-
depth study of the technical configuration by
implementing Failure Mode Evaluation Analysis
(FMEA) in order to construct minimum threshold in
determining the relationship of that component to the
main engine. The FMEA were normally conducted in
an interview session and the mass readings of the
OEM manuals before we could list out the
Maintenance Significant Items (MSI).
Identification of Maintenance Significant Items
(MSI)
As highlighted above, identification of Maintenance
Significant Items (MSI) is one of the key phases in
this paper, which is to screen where the number of
items for analysis is reduced.
In the interview with 10 Ship engineers in this study,
we are able to define the functionality and importance
of each parts as described above, however the
composition of the system is a relatively complex
structure which consists of several subsystems, a
subsystem consists of several components, a
component consists of several parts. Hence, it is
advisable to build a hierarchy tree of the system i.e.
main engine. The figure 10 below was adopted from
(Y., Liu, Jing, Yang, & Zou, 2017) known as the first
screening process.
Figure 10. The first screening process (Y., Liu, Jing,
Yang, & Zou, 2017)
Upon the completion of hierarchy tree in general, the
second screening were conducted in an interview
process with the marine engineers at the engineering
departments where the matrix are set to determine the
level of every component including High(H),
Medium Risk(M) and Low Risk(L) based the
experience or rather the probability of failure and
consequence of failure. In the above figure, shows
that every systems, subsystems and its components
were screened and questions related to the risk matrix
were imposed to the marine engineers involved.
Figure 11. The final frame of system hierarchy tree
i.e. Main engine & MSI
Failure Modes and effects Analysis (FMEA)
Failure Modes and Effects Analysis (FMEA) is a
systematic, proactive method for evaluating a process
to identify where and how it might fail and to assess
the relative impact of different failures, in order to
identify the parts of the process that are most in need
of change. Based on the figure 11 above which clearly
based on the system hierarchy tree and the results of
the first screening, we have successfully performed
the FMEA i.e. Figure below for the relatively
significant items to obtain their function, failure mode
and failure effect on the system.

5. Figure 12. Final outcome –FMEA for Lubrication
system
The Predictive Model: Logistic Regression
The logistic regression formula is derived from the
standard linear equation for a straight line e.g. y = mx
+ b. Using the Sigmoid function in Figure 13 & 14
below, the standard linear formula is transformed to the
logistic regression formula shown in Figure 15. This
logistic regression function is useful for predicting the
class of a binomial target feature.
𝑝 =
1
1 + 𝑒−𝑦
Figure 13. The Sigmoid Function (p)
Figure 14. A Graph that illustrates p outcome equals to
(0, 1)
As for the logit for figure 15, this is interpreted as
taking input log-odds and having output probability
𝑙𝑛 (
𝑝
1 − 𝑝
) = 𝑏0 + 𝑏1 ∗ 𝑥
Figure 15.Inverse of the logistic function
We can now define the inverse of the logistic function.
In above figure 15, the terms are as follows:-
 ln denotes the natural logarithm
 p is the probability that the dependent
variable equals a case, given some
linear combination of the predictors.
 b0 is the intercept from the linear
regression equation
 b1* x is the regression coefficient
multiplied by some value of the
predictor
 base e denotes the exponential function
The Mean Time between Failures (MTTF)
In reliability terms, this function gives us the
probability that a failure occurs between time a and
time b. Hence, this function completely describes the
distribution, and is the basis for almost all of the
familiar reliability and life data functions.
Named for its inventor, Waloddi Weibull, this
distribution is widely used in reliability engineering
and elsewhere due to its versatility and relative
simplicity. In this reliability analysis, we are primarily
concerned with the 2- parameter Weibull probability
density function defined herein as:
𝐹(𝑥) =
𝛽
𝜂
(
𝑥
𝜂
)𝛽−1
𝑒−(𝑥/𝜂)^𝛽
Where:
 𝛽 or beta represents the shape parameter
 𝜂 or eta represents the scale parameter
 x represents the value at which the
function is to be evaluated
The shape parameter, β, determines the overall shape
of the distribution. There are three primary regions in
which β may fall:-
 β < 1.0, indicates infant mortality
 β = 1.0, indicates ‘random’ or ‘constant’
failures.
 β > 1.0, indicates a wear out style of
distribution.
By using the β, it is possible for different parts in the
main engine to exhibit all three of these characteristics
on different components. The figure below refers:-
Figure 16. Bathtub curve description of Beta
parameter in reliability function

6. Conclusion
In Logistic regression, we use data split method i.e.
70% - training and 30% - test, MinMaxscaler method
from scikit-learn library for preprocessing the
variables so that this estimator scales ad translates
each feature individually such that it is in the given
range on the training set, e.g. between zero and one.
Three (3) different scenarios are used for our
allocation of data splitting method as below table 13
refers:-
Table 13. Results – 3 different scenarios tested/results
for accuracy using Confusion Matrix
The F1 score is the harmonic average of the precision
and recall, where F1 score reaches its best value at 1
(perfect precision and recall) and worst at 0. In this
case, we have received F1 score at 0.87 which is
acceptable. The figure below refers:-
Figure 17. Classification report – F1 score at 0.87
We use Weibull package to take the data and calculate
β and ƞ values along with generating any appropriate
plots for display of our data. The fit () method is used
to calculate appropriate both β and ƞ values, which
are then stored into the class instance. In this case, the
fit () is called using maximum likelihood estimation
(MLE) even though there were only 12 samples of the
event failures
Figure 18. Analysis of the Air filter using MLE
method at confidence limit of 95%
Figure 19. Weibull probability plot, probability
Density Function (PDF), Survival Function &
Hazard Function
it was recommended by the marine engineers that a
list of priority on the maintenance items are

7. mandatory and we shall only conduct Weibull
analysis on the parts which has 5 and above recorded
event failures during the 4 months period.
Table 14. Weibull Analysis – 7 list of prioritized parts
within the main engine
According to the table 14 above, it is recommended
for the marine engineers to review the priority list of
parts for maintenance especially when b10 life of that
parts are within 10 hours i.e. Centrifugal Oil filter and
Air filter, which have high chances of failure in the
short run, hence we advise that the marine engineers
should review the quality of the parts as per their
OEM warranty standards and should pursue
alternative supplier for references or replacement
The study of predictive maintenance, using both the
logistic regression as predictor for the next failure and
the deployment of Weibull analysis to calculate the
Mean Time Between Failure (MTBF) or Remaining
Useful Life (RUL). Both methodologies can be used
as an offline analysis tool as well as a real-time
monitoring tool. A combination of both methods and
subsequent inventory analysis and demand
forecasting tool in placed, allows greater capability to
predict ship machinery failures and also enables “ just
in time” maintenance as compared to the
conventional maintenance strategy which follows
both preventive and corrective framework.
With prior knowledge of possible future failure, the
marine engineers responsible for the inspection and
subsequently for the purchase of parts, delivery of
components, docking of vessel, if required, and other
logistical work can plan well ahead of time. It reduces
the risk of corrective maintenance, the shortage of
parts, and last minute procurement. Based on the
prediction, this in turn reduces the need for storage
space as well as for resources to maintain the huge
inventory. The figure 20 below refers
Figure 20. Artificial Intelligence Asset Advanced
Analytics
The study has confirmed that the predictive
methodology is feasible in its limited parameters
analysis as vibration data are important inputs for
predictive modelling as well. Some recommendations
for future work may include the following for a more
conclusive model.
i. The study of detailed OEM’s warranty
for each parts and components in
relation to standard of Weibull analysis
– MTTF recommended hours mean life
of each parts/component
ii. The study of various vibration data from
the different parts of the main engine
and incorporate it into the modelling
techniques for better predictive values.
iii. The architecture for an integrated, real
time, online predictive maintenance
process to be incorporated inside the
ship
We believe that there is more to be done for a much
better conclusive results in our journey of predictive
maintenance for the shipping industry in Malaysia.
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