IAES International Journal of Artificial Intelligence (IJ-AI)

Vol. 13, No. 1, March 2024, pp. 174~184

ISSN: 2252-8938, DOI: 10.11591/ijai.v13.i1.pp174-184  174

Applying Bayesian networks in making intelligent applications

for static and dynamic unbalance diagnosis

Dedik Romahadi1,2, Muhamad Fitri1, Dafit Feriyanto1, Imam Hidayat1, Muhammad Imran3
1
Department of Mechanical Engineering, Faculty of Engineering, Mercu Buana University, Jakarta, Indonesia
2
School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China
3
School of Material Science and Technology, Beijing Institute of Technology, Beijing, China

Article Info ABSTRACT

Article history: One of the problems often encountered in vibration analysis is unbalanced or
imbalanced, namely the occurrence of a shift in the center of mass from the
Received Jul 29, 2022 center of rotation to cause high vibrations. Unbalance itself is divided into
Revised Apr 16, 2023 two, namely static and dynamic unbalance. Identification of the right type of
Accepted May 7, 2023 unbalance must be done because each type of unbalance requires different
handling. Therefore, this study aims to design a system to identify the type of
unbalance based on the required parameters. The system design determines
Keywords: the input and then builds an algorithm by combining vibration analysis
methods and Bayesian networks (BN). Systems and applications are built
Bayesian networks using MATLAB. After the application is finished, testing is carried out using
Intelligent system vibration measurement data obtained from a demo machine that has
Rotating equipment previously been conditioned for damage. The BN method has been
Unbalance successfully applied to the unbalance diagnosis system. When there is
Vibration analysis evidence of large amplitude in 1X the frequency spectrum and the value of the
static phase range, the percentage of static unbalance from 26.8% increases to
75%. The system can predict all testing data quickly and precisely for the six
experiments.
This is an open access article under the CC BY-SA license.

Corresponding Author:
Dedik Romahadi
Department of Mechanical Engineering, Mercu Buana University
Jl. Meruya Selatan No. 1, Kembangan, Jakarta Barat 11650, Indonesia
Email: dedik.romahadi@mercubuana.ac.id

1. INTRODUCTION
The vibration occurs due to damage to shafts, bearings, gears, lack of connection tightness, lack of
smooth lubrication, and the imbalance of rotating machine elements or unbalance [1]–[5]. Vibration in a
machine is very important to pay attention to because, from a vibration, many errors arise and damage the
machine's components. In the past, vibration analysis required full-spectrum instruments to identify frequencies
at which vibrations were dominant [6]–[8]. The operator then compares the peak frequency with the operating
speed and converts it to a graph to determine the possible causes. One of the advantages of the method is that
the operator can gradually learn how a piece of equipment vibrates and why certain problems occur at the same
multiple rotational speeds [9].
The latest generation of vibration meters has more capabilities and automatic functions than its
predecessors. Many units simultaneously display the full vibration spectrum of the three axes, providing an
idea of what is happening with a particular machine [10]–[13]. Although today's vibration meters offer many
automated features and capabilities, they still require a basic understanding of vibration analysis to use them
effectively. Each parameter in the vibration data collection can indicate the type of damage in the machine. For
this reason, proper analysis can produce appropriate recommendations for handling any engine failures that

Journal homepage: http://ijai.iaescore.com

Int J Artif Intell ISSN: 2252-8938  175

occur [14]–[16]. Many vibration measuring instruments do not have intelligent diagnostic features, especially
for the type of unbalanced damage. Users must understand the basic concepts of vibration analysis for types of
damage. An expert also still needs time in the process of diagnosing unbalanced damage, because most tools
only provide unbalance damage parameters and users still need to carry out a series of data analysis processes
until they can decide the unbalance damage that occurred [17].
A Bayesian network (BN) is a probability graphic structure that depicts a causal relationship between
interrelated variables. There are four things that BN can offer as a method: first, BN can easily deal with
incompleteness or problems with data [18]. Second, BN allows one to learn about causal relationships. The
learning process becomes important when we try to understand the domain of the problem. Third, BN can
facilitate the combination of domain knowledge and data. Lastly, BN offers an efficient and principled
approach to avoiding overfitting the data [19]–[24]. Modeling in BN involves two steps, namely creating a
network structure and estimating the probability value of each node. One of the programs that can be used to
build the BN algorithm is to use matrix laboratory (MATLAB). Seeing the advantages that BN has as a
decision-making tool and supported by several journals on the same topic, BN was chosen as a method for
making intelligent applications for the diagnosis of static and dynamic unbalance [25]–[29].
Several studies have been conducted related to this research topic. Research to analyze unbalanced
damage, as well as other damage that may arise when the motor is operating [30]. Research that aims to detect
the location of damage that occurs in the classifier by using vibration signal analysis and measuring the
magnitude of the vibration and presenting it in the form of a frequency domain (spectrum) using the fast Fourier
transform or FFT [31]. The engine condition assessment refers to the ISO 10816-3 standard in velocity and
acceleration modes. Based on the data spectrum analysis, the dominant damage lies in the unbalance rotor.
Associated with expert systems in the field of vibration analysis, Cobb and Li [32], Li et al. [33]; Amrin et al.
[19]; Sahu and Palei [34] emphasize that BN is highly reliable when dealing with uncertainty. BN is a suitable
method for developing intelligent applications in the field of vibration diagnosis.
Based on some of the problems that mention on the second paragraph and the advantages of the BN
method. The author assesses the importance of making an intelligent system to analyze a malfunction in a
machine. For this reason, through this study, the author aims to create an intelligent system to analyze one of
the common malfunctions that occur in a machine, namely unbalance using BN modeling which will be
implemented in the MATLAB application.

2. METHOD
The implementation of this research can be divided into several stages. In general, the stages are
preparing the concept of an algorithm scheme, modeling BN according to the concept of unbalance diagnosis,
making a program in MATLAB, and testing the system that has been made. BNs are constructed using a
statistical method known as Bayes' theorem. This theory employs conditional probability, which is the
likelihood of an event A if it is known that event B has already occurred. The symbol for conditional probability
is P(A|B). The conditional probability can be derived from (1). In addition, there is a joint probability, given
by (P(A∩B)), which represents the likelihood that events A and B will occur. In (2) shows the joint probability.

𝑃(𝐵|𝐴)𝑃(𝐴)
𝑃(𝐴|𝐵) = (1)
𝑃(𝐵|𝐴)𝑃(𝐴)+𝑃(𝐵|𝐴̄)𝑃(𝐴̄)

𝑃(𝐴 ∩ 𝐵) = 𝑃(𝐴|𝐵) × 𝑃(𝐵) (2)

The network topology can be obtained by encoding the expert domain's subjective knowledge. One
arrow can connect the greatest sequence of nodes to the lowest sequence of nodes if a condition is met. Each
arrow is prevented from connecting the lowest node order to the highest node order by the algorithm. If the
scheme is applied to other variables, the network topology may change. Many topologies are possible due to
the fact that multiple arrows can connect various pairings of nodes. Instead, the variable may be subdivided
into many causative variables, with arrows then connecting each cause variable to the corresponding effect
variable.
The BNs network inference can be shown in (3). A basic example involving a three-node network,
X → Y → Z. If we have a proof for the root node, X = x, updating in the same direction as the arc is a
straightforward application of the chain rule based on the network's implied assumption of independence.
Based on the input data obtained, the probability value will be calculated through several stages using the BN
method, starting from determining the parameter value for each damage symptom, then determining the
conditional probability value, after two values are obtained, the system will calculate the combined and
posterior probability values for each type of damage. unbalance adjusted for the BN structure and the posterior
probability value is used as a probability inference of the type of unbalance damage. BN generates relational
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176  ISSN: 2252-8938

information and conditional probabilities through bidirectional propagation between input and output nodes.
Also, in real case implementations, it is common to use multistate nodes. So, from the consultation carried out
by the user, then get the type of unbalance damage that occurred on the machine and the error percentage.

𝐵𝑒𝑙(𝑍) = 𝑃(𝑍 = 𝑋 = 𝑥) = ∑𝑌=𝑦 𝑃(𝑍|𝑌)𝑃(𝑌|𝑋 = 𝑥) (3)

Data preparation in the form of machine specifications and vibration data. This data is prepared as
input and training data when creating the system. Then describe the components of the vibration spectrum to
obtain output data that can be read as input for BN. The entered spectrum data is then processed to find the
frequency of each machine component. Machine specification data will be the reference for frequency
calculations and will be adjusted to the spectrum. If the calculated frequency value is found in the spectrum, it
will be input for evidence to BN, while if the frequency value is not found in the spectrum, it will be input for
no evidence to BN. Then the input data for BN are grouped into 2 types. The frequency line that is proven to
exist according to the frequency calculation becomes input type 1 (true). This input has the requirement that
the frequency amplitude must be 2 mm/s in root mean square (RMS). Meanwhile, for data that is not proven
to have a frequency line according to the calculation or the value of the frequency amplitude < 2 mm/s RMS,
it becomes input type 2 (false). Furthermore, the engine specification data and the input type grouping process
are then entered into the BN calculation so that it will provide information on the diagnosis results such as
vibration status and type of unbalance.

2.1. Tools and materials

A vibration analyzer is a tool that serves to measure the amplitude and frequency of vibration of a
machine (Rotating equipment). Vibration measurement tools as shown in Figure 1 also provide information
about the spectrum data of the vibrations that occur, namely the amplitude data against the frequency, this data
is very useful for analyzing the damage to a machine. The experiment setup as shown in Figures 2 and 3 can
be conditioned to describe static and dynamic unbalance in the machine so that it can validate the diagnosis
results. In addition, it also requires a computer that has Microsoft Belief Networks (MSBNx) and MATLAB
applications installed.

Figure 1. Pruftechnic VibExpert II

Figure 2. Experiment setup

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Vibration data was obtained from the measurement results of the demo machine. Measurements are
made on the horizontal, vertical, and axial axes at the drive end and non-drive end bearing locations. When
taking the overall vibration value and spectrum, the demo machine is conditioned to experience static and
dynamic unbalance problems. The measurement and reference data will be used as a reference in building the
BN system. The finished system will be tested using some random demo machine measurement data. Validate
the results of the system diagnosis against the entered vibration data and look for the cause of an incorrect
diagnostic result is found.

(a) (b)

Figure 3. Condition of unbalance setup, (a) static unbalance and (b) dynamic unbalance

2.2. Tools and materials

Before designing a program in the MATLAB application, it is necessary to determine the probability
of the determinants of unbalance first. To reference the value of probability calculations, the author uses the
MSBNx application. In building the BN structure, the steps are to diagnose engine damage using the vibration
signal. In the first process, it is assumed that the parameters causing the damage are based on a predetermined
probability value. Then build BN in such a way that it can show the type of unbalance damage that occurred
and what actions must be taken to overcome it. The BN causality diagram model is shown in Figure 4 The
parameters used to determine the probability of unbalance are a vibration at 1 time the motor frequency, the
phase difference between horizontal and vertical, the phase difference between the drive end and non-drive
end bearings, and the ratio of the thickness and diameter of the impeller. Prior probability values can be seen
in Table 1.

Table 1. Prior probability

Node Name Event Percentage (%)
Exist 20
High 1X
Not exist 80
Thick < Diameter 60
T vs D
Thick > Diameter 40
20º ≥ Phase ≥ 340º 50
Phase DE NDE
20º ≤ Phase ≤ 340º 50
70º ≤ Phase ≤ 110º 50
Phase HV
70º ≥ Phase ≥ 110º 50

2.3. Prior and conditional probability

The BN structure consists of 6 nodes as shown in Figure 4, with 4 parameters specifying the type of
unbalance. The first node is High_1X which is an event if a high amplitude is found that occurs at 1 time the
engine revolutions per minute (RPM). The TvsD is the ratio of the thickness and diameter of the impeller. If
the impeller thickness is equal to or greater than the impeller diameter, it will be one of the supporting factors
for dynamic unbalance. While the Phase_DE_NDE and Phase_HV nodes represent the large difference in the
vibration phase at the two measurement points. Phase_DE_NDE is the phase difference at two bearing
locations, namely DE and NDE impeller, in which both measurement locations are the same, namely on the
horizontal axis (DE H and NDE H) or vertically (DE V and NDE V). While the Phase_HV node is the phase
difference at one bearing location which is carried out on the horizontal and vertical axes (DE H with DE V or

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NDE H with NDE V). After finishing building the BN structure, the next step is to determine the probability
value of each combination of event components that occur. The conditional probability table (CPT) for static
unbalance and dynamic unbalance nodes can be seen in Table 2 and Table 3.

Figure 4. Bayesian network structure

Table 2. CPT of static unbalance

Parent Node (s) P (Static Unbalance) (%)
High 1X T vs D Phase DE NDE Phase HV Yes No
Exist T<D 20º ≥ P ≥ 340º 70º ≤ P ≤ 110º 90 10
70º ≥ P ≥ 110º 80 20
20º ≤ P ≤ 340º 70º ≤ P ≤ 110º 80 20
70º ≥ P ≥ 110º 70 30
T>D 20º ≥ P ≥ 340º 70º ≤ P ≤ 110º 70 30
70º ≥ P ≥ 110º 60 40
20º ≤ P ≤ 340º 70º ≤ P ≤ 110º 50 50
70º ≥ P ≥ 110º 40 60
Not exist T<D 20º ≥ P ≥ 340º 70º ≤ P ≤ 110º 30 70
60º ≥ P ≥ 110º 20 80
20º ≤ P ≤ 340º 70º ≤ P ≤ 110º 20 80
70º ≥ P ≥ 110º 10 90
T>D 20º ≥ P ≥ 340º 70º ≤ P ≤ 110º 10 90
70º ≥ P ≥ 110º 10 90
20º ≤ P ≤ 340º 70º ≤ P ≤ 110º 10 90
70º ≥ P ≥ 110º 10 90

Table 3. CPT of dynamic unbalance

Parent Node (s) P (Dynamic Unbalance) (%)
High 1X T vs D Phase DE NDE Phase HV Yes No
Exist T<D 20º ≥ P ≥ 340º 70º ≤ P ≤ 110º 30 70
70º ≥ P ≥ 110º 40 60
20º ≤ P ≤ 340º 70º ≤ P ≤ 110º 40 60
70º ≥ P ≥ 110º 60 40
T>D 20º ≥ P ≥ 340º 70º ≤ P ≤ 110º 30 70
70º ≥ P ≥ 110º 70 30
20º ≤ P ≤ 340º 70º ≤ P ≤ 110º 70 30
70º ≥ P ≥ 110º 90 10
Not exist T<D 20º ≥ P ≥ 340º 70º ≤ P ≤ 110º 10 90
70º ≥ P ≥ 110º 10 90
20º ≤ P ≤ 340º 70º ≤ P ≤ 110º 10 90
70º ≥ P ≥ 110º 20 80
T>D 20º ≥ P ≥ 340º 70º ≤ P ≤ 110º 10 90
70º ≥ P ≥ 110º 20 80
20º ≤ P ≤ 340º 70º ≤ P ≤ 110º 20 80
70º ≥ P ≥ 110º 30 70

3. RESULTS AND DISCUSSION

3.1. Probability updates
Each piece of evidence received by the network will affect the percentage value of each node by the
CPT value that has been applied. The highest percentage of the yield node, namely static unbalance and
dynamic unbalance will be the basis for the system to determine the type of damage that occurs to the machine.
Therefore, it is necessary to ensure that the CPT value is correct when building the BN so that the prediction
results are correct in all conditions of the input data entered. Figure 5 is showing the updated probability of the

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computational result of the BN algorithm without any evidence provided. It can be seen without proof that the
static unbalance probability value is 26.8% and produces a dynamic unbalance probability of 22.7%. All
probability values are still less than 50%, this indicates that there is no damage to the centrifuge.
In ensuring that the prediction results are correct based on the concept of vibration analysis, a series
of experiments are carried out and validate the results. Figure 6 shows the renewal of confidence because new
information is provided with evidence of large amplitude symptoms at the 1X frequency and the phase on the
horizontal-vertical axis is more than or equal to 70º and less than or equal to 110º. The probability value of
static unbalances increases to 75% and dynamic unbalances to 41%. So, it can be concluded that the machine
has a static unbalance problem because it has a larger percentage compared to the dynamic unbalance
percentage value. If we review the results of the system from the inputs given, the results are same by theory
and manual analysis. Probability updates due to the new information provided with evidence of large amplitude
at 1X frequency, impeller thickness smaller than or equal to impeller diameter, and phase in DE-NDE bearings
smaller than or equal to 20º and more than or equal to 340º as shown in Figure 7. It can be seen that the
probability value of static unbalance increases to 80% and the percentage of dynamic unbalance becomes 40%.
From these results, it can be concluded that the damage to the machine is static unbalance.

Figure 5. Probability updates without evidence

Figure 6. Probability updates with evidence of High 1X and Phase HV

Figure 7. Probability updates with static unbalance spectrum

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3.2. Designing of interface

The application interface design is shown in Figures 8-10. The application is made simple which aims
to make it easy for users to enter data and understand the information generated by the system. Basic facilities
are provided to obtain important information on the type of unbalanced damage to the machine. The application
is designed to be able to display the original spectrum data in graphical form, display the results of the BN
algorithm calculations for the prediction of the type of unbalance in the form of a bar graph and display
important information about the diagnosis results. In addition, the application is equipped with a description of
the image related to the type of damage that occurred. There are four conditions, if the data processed by BN
produces static unbalance, the recommendation given by the system is to do balancing in single plane mode.
Meanwhile, if the result of BN processing is dynamic unbalance, the system recommends balancing with two
plane mode. If the spectrum data shows a large amplitude but the entered RMS value is small, then the system
will recommend re-checking the input data and doing the analysis manually. This also applies if the RMS value
is large, but no large amplitude is found in the spectrum. Facilities like reset are also added so that users can
restart the diagnosis easily. The appearance of the application before running is shown in Figure 8.

Figure 8. Application interface design

3.3. Software validation

Software feasibility testing by implementing and checking the system that has been designed in a
series of validation processes. This is done to find out if the system can run well, and if all the features are as
desired. This section will explain the implementation of an intelligent system for diagnosing machine failures
based on the analysis and design that has been carried out previously.

3.4. Software validation

Software feasibility testing by implementing and checking the system that has been designed in a
series of validation processes. This is done to find out if the system can run well, and if all the features are as
desired. This section will explain the implementation of an intelligent system for diagnosing machine failures
based on the analysis and design that has been carried out previously. After the implementation phase is
complete, then proceed with the implementation testing that has been done. Software testing is carried out to
ensure that the system built is by the results of the analysis and design so that a conclusion can be made.
Validation is done by seeing whether the results of the system diagnosis with the actual conditions are the
same, based on the symptoms that have been obtained from the data that has been entered.
The test uses vibration data whose measurements are taken from a balancing and alignment demo
machine where the data contains several types of measurements such as vibration value, the phase between the
horizontal-vertical axes, the phase between the two bearings, and the spectrum. This data will be used for input
and to determine whether it is by the conditions of the demo machine. Read the results of the diagnosis in the
form of the type of damage that occurred and what actions the user must take. Figure 9 is a trial operation of
the application using data from the measurement results of the demo machine which is set to experience static
unbalance. As seen in the input data column, the demo machine has a rotational speed of 1,482 RPM, a vibration

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value of 5 mm/s RMS, an impeller thickness of 20 mm, an impeller diameter of 100 mm, the phase between
the two bearings on the same axis is 12º and the phase between the horizontal and vertical axes is 98º. The
column of the spectrum graph shows a large amplitude at 1 time the impeller frequency. Then the system
produces a diagnosis that the problem that occurs is static unbalance with a percentage value of 90%. The
system recommends balancing using single plane mode.

Figure 9. Test results with static unbalance

The application operation experiment using the data from the measurement results of the demo engine
which is set to experience dynamic unbalance is shown in Figure 10. As seen in the input data column, the
vibration value is 4.7 mm/s RMS, the impeller thickness is 120 mm, the impeller diameter is 100 mm, and the
phase between the two bearings on the same axis is 45º and the phase between the horizontal and vertical axes
is 15º. The column of the spectrum graph shows a large amplitude at 1 time the impeller frequency. The system
produces a diagnosis that the problem that occurs is dynamic unbalance with a percentage value of 90%. The
system recommends being balanced using the two-plane mode. The results of all experimental data in detail
can be seen in Table 4.

Figure 10. Test results with dynamic unbalance

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Table 4. Summary of experimental results

Data Input Results
No.
RMS (mm/s) T D P2B PHV Spectrum Actual System
1. 5 20 100 12º 98º 1X Static Unbalance Static Unbalance
2. 4.7 120 100 45º 15º 1X Dynamic Unbalance Dynamic Unbalance
3. 1.8 50 100 12º 50º None Good Good
4. 4.3 20 100 185º 95º 1X, 2X, 3X Misalignment Other Damage
5. 5.2 100 100 115º 132º 1X Dynamic Unbalance Dynamic Unbalance
6. 4.3 20 100 5º 100º 1X Static Unbalance Static Unbalance

The demo machine before a series of measurements is set up with several conditions and will be
compared with the predicted problem results issued by the system, as shown in Table 4. The system diagnosis
results are by the actual state of the demo machine. In the fourth experiment, the results of the system diagnosis
are not the same as the actual conditions, this is because the system is only prepared to read the unbalance
problem. Even so, the system can still recognize that misalignment damage is another type of damage.
Therefore, it can be concluded that the system that has been designed can predict the state of the machine based
on vibration data quickly and accurately.

4. CONCLUSION
BN-based software for the prediction of unbalance damage has been developed. The application of
BNs in intelligent system design begins with making network modeling. Then the percentage of unbalance
values is made based on the value of each parameter using the MSBNx software. The percentage results that
have been collected will be used as a reference when creating the BN algorithm in the MATLAB application.
In addition, a function is also made to enter data that will be used as the basis for calculating the percentage of
unbalance in the system. Then an application was made with the AppDesigner menu in MATLAB so that the
program that had been created could be installed on the computer and operated easily. BN Modeling was
successfully built based on the concept of vibration analysis. Four nodes have been defined as input parameters
and two nodes as output. The six nodes are connected according to their function so that it becomes a system
that can predict the type of unbalanced damage to the machine. From six trials using different data, all data
entered can be predicted by the system quickly and accurately. All software features also work properly.

ACKNOWLEDGEMENTS
The authors sincerely thank the financial support provided by Mercu Buana University, contract No.
02-5/535/B-SPK/III/2022.

REFERENCES
[1] K. Ágoston, “Studying and measuring system for motor base unbalance,” Procedia Manufacturing, vol. 46, pp. 391–396, 2020,
doi: 10.1016/j.promfg.2020.03.057.
[2] G. Fan, J. Li, and H. Hao, “Vibration signal denoising for structural health monitoring by residual convolutional neural networks,”
Measurement: Journal of the International Measurement Confederation, vol. 157, p. 107651, Jun. 2020,
doi: 10.1016/j.measurement.2020.107651.
[3] A. Khan, D. K. Ko, S. C. Lim, and H. S. Kim, “Structural vibration-based classification and prediction of delamination in smart
composite laminates using deep learning neural network,” Composites Part B: Engineering, vol. 161, pp. 586–594, Mar. 2019,
doi: 10.1016/j.compositesb.2018.12.118.
[4] F. Anggara, D. Romahadi, A. L. Avicenna, and Y. H. Irawan, “Numerical analysis of the vortex flow effect on the thermal-hydraulic
performance of spray dryer,” Sinergi, vol. 26, no. 1, p. 23, Feb. 2022, doi: 10.22441/sinergi.2022.1.004.
[5] M. Fitri, T. Susilo, D. Feriyanto, and D. M. Zago, “Effect of morphology and percentage of second phase content of coconut coir
on the impact strength of epoxy resin composites,” Volatiles & Essent. Oils, vol. 8, no. 6, pp. 3880–3894, 2021.
[6] R. Karthik, S. S. Kumar, and A. T. Selvan, “Standards for vibration analysis,” International Conference on Systems, Science,
Control, Communication, Engineering and Technology, vol. 02, pp. 548–553, 2016.
[7] D. Romahadi, A. A. Luthfie, and L. B. Desti Dorion, “Detecting classifier-coal mill damage using a signal vibration analysis,”
Sinergi, vol. 23, no. 3, p. 175, Sep. 2019, doi: 10.22441/sinergi.2019.3.001.
[8] Z. Liu et al., “Traveling wave resonance analysis of flexible spur gear system with angular misalignment,” International Journal of
Mechanical Sciences, vol. 232, p. 107617, Oct. 2022, doi: 10.1016/j.ijmecsci.2022.107617.
[9] C. Zhou et al., “Vibration singularity analysis for milling tool condition monitoring,” International Journal of Mechanical Sciences,
vol. 166, p. 105254, Jan. 2020, doi: 10.1016/j.ijmecsci.2019.105254.
[10] J. Jiang, H. Yang, G. Chen, and K. Wang, “Numerical and experimental analysis on the vibration and radiated noise of the cylinder
washing machine,” Applied Acoustics, vol. 174, p. 107747, Mar. 2021, doi: 10.1016/j.apacoust.2020.107747.
[11] F. Li, W. Wang, S. Dubljevic, F. Khan, J. Xu, and J. Yi, “Analysis on accident-causing factors of urban buried gas pipeline network
by combining DEMATEL, ISM and BN methods,” Journal of Loss Prevention in the Process Industries, vol. 61, pp. 49–57, Sep.
2019, doi: 10.1016/j.jlp.2019.06.001.

Int J Artif Intell, Vol. 13, No. 1, March 2024: 174-184

Int J Artif Intell ISSN: 2252-8938  183

[12] Y. Luo et al., “A situational awareness Bayesian network approach for accurate and credible personalized adaptive radiotherapy
outcomes prediction in lung cancer patients,” Physica Medica, vol. 87, pp. 11–23, Jul. 2021, doi: 10.1016/j.ejmp.2021.05.032.
[13] R. Karsi, M. Zaim, and J. El Alami, “Assessing naive Bayes and support vector machine performance in sentiment classification on
a big data platform,” IAES International Journal of Artificial Intelligence (IJ-AI), vol. 10, no. 4, p. 990, Dec. 2021,
doi: 10.11591/ijai.v10.i4.pp990-996.
[14] G. Bruand, F. Chatelain, P. Granjon, N. Martin, and C. Duret, “Reconstructing shaft orbit using angle measurement to detect bearing
faults,” Mechanical Systems and Signal Processing, vol. 139, p. 106561, May 2020, doi: 10.1016/j.ymssp.2019.106561.
[15] D. F. Plöger, P. Zech, and S. Rinderknecht, “Vibration signature analysis of commodity planetary gearboxes,” Mechanical Systems
and Signal Processing, vol. 119, pp. 255–265, Mar. 2019, doi: 10.1016/j.ymssp.2018.09.014.
[16] M. Vishwakarma, R. Purohit, V. Harshlata, and P. Rajput, “Vibration analysis and condition monitoring for rotating machines: a
review,” Materials Today: Proceedings, vol. 4, no. 2, pp. 2659–2664, 2017, doi: 10.1016/j.matpr.2017.02.140.
[17] J. Qi, M. Miyashita, T. Ogawa, H. Naito, and K. Sasaki, “Resonance frequency analysis for evaluation of the connecting condition
between fixed prostheses and their abutment teeth: An in vitro and finite element analysis study,” Journal of Prosthetic Dentistry,
Apr. 2022, doi: 10.1016/j.prosdent.2022.03.005.
[18] X. Zhang and S. Mahadevan, “Bayesian network modeling of accident investigation reports for aviation safety assessment,”
Reliability Engineering and System Safety, vol. 209, p. 107371, May 2021, doi: 10.1016/j.ress.2020.107371.
[19] A. Amrin, V. Zarikas, and C. Spitas, “Reliability analysis and functional design using Bayesian networks generated automatically
by an ‘Idea Algebra’ framework,” Reliability Engineering and System Safety, vol. 180, pp. 211–225, Dec. 2018,
doi: 10.1016/j.ress.2018.07.020.
[20] M. Bessani, J. A. D. Massignan, T. M. O. Santos, J. B. A. London, and C. D. Maciel, “Multiple households very short-term load
forecasting using Bayesian networks,” Electric Power Systems Research, vol. 189, p. 106733, Dec. 2020,
doi: 10.1016/j.epsr.2020.106733.
[21] F. G. Cozman and D. D. Mauá, “The complexity of Bayesian networks specified by propositional and relational languages,”
Artificial Intelligence, vol. 262, pp. 96–141, Sep. 2018, doi: 10.1016/j.artint.2018.06.001.
[22] M. J. Jafari, M. Pouyakian, A. khanteymoori, and S. M. Hanifi, “Reliability evaluation of fire alarm systems using dynamic Bayesian
networks and fuzzy fault tree analysis,” Journal of Loss Prevention in the Process Industries, vol. 67, p. 104229, Sep. 2020,
doi: 10.1016/j.jlp.2020.104229.
[23] Y. Yang, X. Gao, Z. Guo, and D. Chen, “Learning Bayesian networks using the constrained maximum a posteriori probability
method,” Pattern Recognition, vol. 91, pp. 123–134, Jul. 2019, doi: 10.1016/j.patcog.2019.02.006.
[24] A. Khosbayar, J. Valluru, and B. Huang, “Multi-rate Gaussian Bayesian network soft sensor development with noisy input and
missing data,” Journal of Process Control, vol. 105, pp. 48–61, Sep. 2021, doi: 10.1016/j.jprocont.2021.07.003.
[25] P. Boutselis and K. McNaught, “Using Bayesian networks to forecast spares demand from equipment failures in a changing
service logistics context,” International Journal of Production Economics, vol. 209, pp. 325–333, Mar. 2019,
doi: 10.1016/j.ijpe.2018.06.017.
[26] D. Dinis, A. Barbosa-Póvoa, and Â. P. Teixeira, “Valuing data in aircraft maintenance through big data analytics: A probabilistic
approach for capacity planning using Bayesian networks,” Computers and Industrial Engineering, vol. 128, pp. 920–936,
Feb. 2019, doi: 10.1016/j.cie.2018.10.015.
[27] C. Liu, Y. Wang, X. Li, Y. Li, F. Khan, and B. Cai, “Quantitative assessment of leakage orifices within gas pipelines using a
Bayesian network,” Reliability Engineering and System Safety, vol. 209, p. 107438, May 2021, doi: 10.1016/j.ress.2021.107438.
[28] D. Romahadi, A. A. Luthfie, W. Suprihatiningsih, and H. Xiong, “Designing expert system for centrifugal using vibration signal
and Bayesian networks,” International Journal on Advanced Science, Engineering and Information Technology, vol. 12, no. 1,
pp. 23–31, Jan. 2022, doi: 10.18517/ijaseit.12.1.12448.
[29] A. A. Ojugo and A. O. Eboka, “Empirical Bayesian network to improve service delivery and performance dependability on
a campus network,” IAES International Journal of Artificial Intelligence, vol. 10, no. 3, pp. 623–635, Sep. 2021,
doi: 10.11591/ijai.v10.i3.pp623-635.
[30] M. Shihan, J. Chandradass, and T. T. M. Kannan, “Investigation of vibration analysis during end milling process of monel alloy,”
Materials Today: Proceedings, vol. 39, pp. 695–699, 2020, doi: 10.1016/j.matpr.2020.09.193.
[31] T. Wang, Q. Han, F. Chu, and Z. Feng, “Vibration based condition monitoring and fault diagnosis of wind turbine planetary gearbox:
A review,” Mechanical Systems and Signal Processing, vol. 126, pp. 662–685, Jul. 2019, doi: 10.1016/j.ymssp.2019.02.051.
[32] B. R. Cobb and L. Li, “Bayesian network model for quality control with categorical attribute data,” Applied Soft Computing Journal,
vol. 84, p. 105746, Nov. 2019, doi: 10.1016/j.asoc.2019.105746.
[33] H. Li, C. Guedes Soares, and H. Z. Huang, “Reliability analysis of a floating offshore wind turbine using Bayesian Networks,”
Ocean Engineering, vol. 217, p. 107827, Dec. 2020, doi: 10.1016/j.oceaneng.2020.107827.
[34] A. R. Sahu and S. K. Palei, “Real-time fault diagnosis of HEMM using Bayesian Network: A case study on drag system of dragline,”
Engineering Failure Analysis, vol. 118, p. 104917, Dec. 2020, doi: 10.1016/j.engfailanal.2020.104917.

BIOGRAPHIES OF AUTHORS

Dedik Romahadi holds a Master of Science (M.Sc.) in Mechanical Engineering.

He is currently pursuing his Ph.D. Degree in Mechanical Engineering at Beijing Institute of
Technology, Beijing, China. He is also a permanent lecturer at Mercu Buana University,
Jakarta, Indonesia, since August 2018. Signal processing and machine learning are
components of his research. His study has been published in a variety of indexed and non-
indexed national and international publications. He can be contacted at email:
dedik.romahadi@mercubuana.ac.id.

Applying Bayesian networks in making intelligent applications for static and dynamic … (Dedik Romahadi)
184  ISSN: 2252-8938

Muhamad Fitri has completed his bachelor’s degree in Mechanical Engineering

from Andalas University in 1994, master's degree from Batam University in 2009 and
doctoral degree from Tun Hussein Onn Universiti Malaysia (UTHM) in 2019. He is currently
working as a Lecturer in the Mechanical Engineering Department, of Engineering Faculty,
University of Mercubuana, Jakarta. He is also active in conducting research especially in
advanced material engineering field. He has published his research results in various
accredited local journals, Scopus indexed International Proceedings and also Scopus indexed
international journals. He can be contacted at email: muhamad.fitri@mercubuana.ac.id.

Dafit Feriyanto has completed bachelor’s degree in Mechanical Engineering

Department, Mataram University in 2012. Then master and doctoral degree from Universiti
Tun Hussein Onn Malaysia (UTHM) in 2015 and 2018, respectively. He is currently active
in teaching and conducting research in Mechanical Engineering Department, Mercu Buana
University. His research field on Advanced Material and Green technology has been
published in various indexed and non-indexed national and international journals. He can be
contacted at email: dafit.feriyanto@mercubuana.ac.id.

Imam Hidayat has completed bachelor’s degree in Mechanical Engineering

Department, Brawijaya University in 2002. master's degree from Universitas Indonesia
(University of Indonesia) in 2008 and doctoral degree from Beijing University of Technology
in 2019. He is currently active in teaching and conducting research in the Mechanical
Engineering Department, Mercu Buana University. His research field on Advanced Material
Engineering and Machining Engineering has been published in various indexed and non-
indexed national and international journals. He can be contacted at email:
imam.hidayat@mercubuana.ac.id.

Muhammad Imran has been working on solar energy since 2010. He is

currently doing his Ph.D. at the Beijing Key Laboratory of Environmental Science and
Engineering at the Beijing Institute of Technology, China, and has a history of working in
various prestigious organizations. His research field is Failure Mechanisms and Recycling
Key Materials for Lithium-ion Batteries; the synthesis and simulation of new electrolyte
materials that are assumed to be more efficient and environment friendly for energy storage
devices; the enhancement of the efficiency of solar cell and the improvement of the problems
and defects in the design and installation of solar systems. He has a deep knowledge of
innovative design and solutions based on solar and renewable energy systems. In line with
his educational and research work, Muhammad Imran also connected globally and
collaborated on several dynamic projects in Beijing on green energy solutions. He can be
contacted at email: Muhammad.imranphys2@gmail.com.

Int J Artif Intell, Vol. 13, No. 1, March 2024: 174-184