Analysis of the Sw-420 Vibration Sensor Performance on Vibration

Tools by using a Fuzzy Logic Method

Ikhwan El Akmal Pakpahan, Poltak Sihombing, and Mahyuddin K. M. Nasution

Informatics Engineering, Faculty of Information Technology and Computer Science, University of North Sumatera, Medan,

Indonesia

Keywords: Arduino uno, fuzzy logic, sw-420, vibration analysis, vibration sensor.

Abstract: Vibration sensor Sw-420 is one of the most widely used sensor modules because it is easy to learn and to

install. This sensor is also compatible with Arduino Uno microcontroller board. This sensor has two types of

output, namely digital output (0/1) and analog output (voltage). However, for specific purposes such as

security and industry, more than just vibration detection is needed. The system used must be smart to

distinguish vibrations due to errors / damage or just technical / accidental errors. The vibration received by

the sensor must be classified properly. In this study, the fuzzy logic method as a decision support system to

help determine the appropriate vibration classification was applied. The reason for using the fuzzy logic

method was that it is flexible, easy to understand, and is able to produce values that are more specific than

just 0 and 1 values according to the needs of the SW-420 vibration sensor. With the fuzzy logic method, it

can be seen that the sensor can classify vibrations into 5 levels, very weak, weak, moderate, strong and very

strong.

1 INTRODUCTION

Vibration is something that is often found in everyday

life. When an object vibrates, it affects not only the

object itself but also the objects around it. Apart from

physical contact, a vibration can also be detected from

the sound generated through the vibration. This

happens because these vibrations create friction for

the surrounding objects and the object itself. Through

a vibration, information about what is happening with

the object can be received so that humans can

estimate the possibility of what is happening due to

these vibrations.

The vibrations that occur will have different

effects on different objects. A large tremor on the side

of a cross-city road may not have a significant effect

as it is common (many large capacity cars pass). But

vibrations of that magnitude would be a big problem

if they happened under a quiet house and away from

the streets. Because it requires a system or a tool to

classify the vibrations that occur in order to determine

whether the vibration is a problem that needs to be

resolved or not and how to solve it.

For many years, Fuzzy Logic has been considered

as a control algorithm with The Fuzzy Logic

Controller. This controller has been widely utilized

for the active vibration control of engineering

structures. The capability of using fuzzy control

strategies in vibration control of civil engineering

structures with active control systems was established

by focusing on the seismic response of frame

structures due to multiple earthquake records with an

active mass driver system as a control device on the

top story Azizi. M., Ejlali. R. G., Ghasemi. S. A. M.,

Talatahari. S. (2019).

Research by Julio Fajar Saputra, et al. (2018)

show the advantages of using LoRa (Long Range)

connections for wider distances. LoRa-based data

communication testing uses LOS and NLOS

scenarios, data emergence is measured using a web

data center. The drawback is that it requires greater

costs and more complex programming because it uses

two microcontrollers, namely Arduino Uno and

Raspberry Pi.

Widya Purnamasari and Romi Wijaya's (2017)

research shows the advantages of using more than one

sensor to increase security as well as a way to monitor

which parts of the vibration are detected. This system

is also equipped with a database that is useful for

storing data and processing time. The drawback lies

in the use of a PC for monitoring. PC is not a device

that has high mobility so it will indirectly force users

Pakpahan, I., Sihombing, P. and Nasution, M.

Analysis of the Sw-420 Vibration Sensor Performance on Vibration Tools by using a Fuzzy Logic Method.

DOI: 10.5220/0010336000003051

In Proceedings of the International Conference on Culture Heritage, Education, Sustainable Tourism, and Innovation Technologies (CESIT 2020), pages 543-550

ISBN: 978-989-758-501-2

543

to stay around the PC to monitor the house. PC also

requires a lot of power so it will require additional

costs for electrical power in case of a power cut

(difficult for users to monitor the system).

Poltak Sihombing, Jos Timanta Tarigan et al.

(2019) presents a security system that offers better

efficiency. It has the ability to identify when an

intruder enters the warehouse and send reports to

registered users via short message service (SMS).

Information is based on vibration and infrared to

detect intruders. They specifically use the Raspberry

Pi as the system microcontroller because of its ease of

use. In the test case, they implemented a security

system built into the warehouse and observed the

effectiveness of the system in detecting intruders.

They test several possible methods of intruders to

enter the warehouse and log system responses. The

results show that the system has recorded the incident

effectively and sent a report via SMS to registered

users. The difference in average time between an

intrusion event and a notification received by a user

is 5 to 10 seconds.

In the research of Ikhwan El Akmal Pakpahan

(2017) it has advantages, namely the monitoring

process using a smartphone which can increase user

mobility and minimize the possibility of users not

hearing alarms sounding (alarms do not only come

from bells but also from smartphones).

From this research, the author wants to develop

the research of Ikhwan El Akmal Pakpahan by adding

a fuzzy method so that the resulting system is better

at monitoring existing vibrations as well as analyzing

the fuzzy logic method on the SW-420.

2 METHODOLOGY

2.1 Research Methodology

In this study, the capabilities of the SW-420 vibration

sensor with the fuzzy logic method so that the

received vibrations can be classified into 5 parts,

namely very weak, weak, moderate, strong, and very

strong are developed. To aid in vibration

classification, an algorithm for determining whether

a received vibration is valid or accidental is

constructed. This then becomes one of the 2 factors

that determine the type of vibration classification

received by the sensor.

2.2 Research Design

In this study, the fuzzy logic method as a decision

support system to divide the classification of

vibrations received by the sw-420 vibration sensor

based on two variables (namely the average value of

analog data and the total data filled with vibration

values from the 15 data slots provided) was applied.

The following is the research design process:

1. Analyze the maximum value of analog data

input that can be received by the sw-420

vibration sensor.

2. Adjust and adjust the sensitivity of the sensor

to received vibrations.

3. Applying the fuzzy logic method to the

Arduino Uno microcontroller connected to the

sensor.

4. Analyze the results obtained by the vibration

sensor after adding the fuzzy logic method as a

decision support system.

2.3 Research Stages

The stages of the research to be carried out can be

seen in Figure 1.

Figure 1: Research Stages.

The stages of the research to be carried out can be

seen in Figure 1. Figure 1 is the stage carried out in

the research: the first stage was to determine the

Background, Formulation of the

Problems and Objectives

Data Collection

Fuzz

ic Anal

sis

Fuzzyfication

Inference Process

Defuzz

fication

Analysis of Utilizing Fuzzy

Tsukamoto

Conclusion

CESIT 2020 - International Conference on Culture Heritage, Education, Sustainable Tourism, and Innovation Technologies

544

background of the reasons for conducting the

research, formulate some things that have been done

and make the goals to be achieved. Furthermore, the

data were collected to be processed using fuzzy logic

with the Tsukamoto method. The second stage was

fuzzification and performs an inference process from

the data that has been obtained for testing using the

applied Tsukamoto method. In the last stage, test

results and conclusions are obtained.

2.4 System Design

In system design, the system that has been designed

will be described and explained including the steps

that must be taken so that the system runs as desired,

starting from sensor detection, sending data from the

sensor to the microcontroller to the output after being

classified by the fuzzy logic method.

The Arduino Uno vibration sensor and

microcontroller communicate using a jumper cable

on the specified port. Meanwhile, to display the

results of vibration classification with fuzzy logic as

a decision support system, Arduino Uno was

connected to a laptop / PC. Furthermore, the Arduino

IDE application results of vibration classification can

be seen on the serial monitor.

The following are the stages of system design

being developed:

1. Initially, the Sw-420 vibration sensor data was

received by the Arduino Uno Microcontroller.

2. The data was classified into several parts, such

as analog data with a value of more than 10 will

be entered and processed in the variable

"content data". When the total sensor data

received has reached 15 data, the results of the

average value are accommodated in the "analog

data" variable.

3. After the required data is sufficient, the next

step was to determine the degree of

membership, namely determining whether the

data is included in a certain membership or not.

4. The next step was to calculate the predicate of

the rule in the form of calculations performed

to determine the predicate of the received data

rule.

5. Defuzzification was the process of converting

data from fuzzy data to real data.

6. The final step was to display the results of the

data classification from the vibration sensor.

2.5 Flowchart

The steps of the program process can be seen in

Figure 2.

Start

Initializationof

inputdata

ifdatagetar>1

Tambahgetar+=

datage tar ,

Hitung+=1

Initializationof

inputdata

Ifdatagetar>10

Dataisi+=1

Ifhit ung== 16

Hitung=0;

Dtotalgetar=

totalgetar/15

Totalgetar=0;

Ifdtotalgetar<=

Serial.println=

“Tidakterdeteksi

getaran”

Elseif

dtotalgetar<50‐

250

Elseif

dtotalgetar>250

‐300

Serial.println=Data

terklasifikasi

RENDAHkurva

PUNCAK

(X1&X2=1)

Serial.println=Data

terklasifikasi

RENDAH_KT&

SEDANG_KN

Elseif

Dtotalgetar

>300‐550

Serial.println=Data

terklasifikasiTINGGI

kurvaPUNCAK

(X1&X2=1)

Elseif

dtotalgetar>550

‐600

Serial.println=Data

terklasifikasi

Tinggi_KT&

BESAR_KN

Elseif

dtotalgetar>600

Dataterkl asif ikasi 

TINGGIkurva

PUNCAK

(X1&X2=1)

Else=

pri nt(“dtotal getar”)

GTR

X1=(d‐x)/(d‐c)

X2=(x‐a)/(b‐a)

X1=(d‐x)/(d‐c)

X2=(x‐a)/(b‐a)

Figure 2: Flowchart.

2.6 Arduino Uno

Arduino Uno, an open-source platform, accounts for

the data acquisition hardware in our toolbox.

Different types of connection ports, including digital

input/output, PWM output, UART TTL (5V) serial

communication, and analog input, make the Arduino

Uno board powerful and cost-effective hardware for

data collection purposes. The Arduino Uno board has

an Atmel ATmega328 microcontroller which can be

Analysis of the Sw-420 Vibration Sensor Performance on Vibration Tools by using a Fuzzy Logic Method

545

programmed in C/C++ language through an

integrated development environment (IDE).

Regulated 5 V and 3.3 V outputs can be obtained from

the Arduino board to provide the supply voltage for

particular sensors. The Arduino Uno supports 6

analog input pins that read data in the range of 0–5V

with the resolution of 10 bits (Karami. M.,

McMorrow. G. V., Wang. L., 2018).

2.7 Vibration Sensor

A vibration sensor is a sensor that can measure the

vibration of an object whose data will be processed

for experimental purposes or used to anticipate a

possible danger. It has measurement accuracy and

increasing the sensitivity range. One type of vibration

sensor is the SW-420 sensor (Deviana. H, Amin. M.

M., Sandy R., et al., 2019).

2.8 SW-420 Vibration Sensor

In this study the SW-420 series was used as a research

variable. SW-420 is a vibration sensor that can detect

vibrations from various angles. When the sensor does

not receive vibrations, the electronic components of

the sensor will function as a switch and will be closed.

Meanwhile, when the sensor receives a vibration /

shock, the switch will open and close at the transfer

rate following the vibration (Siahaan. A.O, 2017).

This module features an adjustable potentiometer,

a vibration sensor, and an LM393 comparator chip to

give an adjustable digital output based on the amount

of vibration. The potentiometer can be adjusted to

both increase and decrease the sensitivity to the

desired amount (Jagdale. S. B., Sali. M. R., Kulkarni.

S. D. et al., 2019).



Image of the SW-420 vibration sensor can be seen

in Figure 3:

Figure 3: SW-420 vibration sensor.

This circuit receives vibrations through a metal

buoy inside the sensor. It also has 2 types of output,

namely: digital output (based on values 0 and 1) and

analog output (value is in the form of received

voltage).

Table 1: SW-420 sensor specification.

No Specification Information

1 Volta

e use

3,3V DC

–

5V DC

2 Output Digital (0 and 1)

Analo

3 Sensor size 3,2 cm x 1,4 cm

4 Detection distance 760 nm

–

1100 n

5 Detection an

le 60 de

ree

6 Si

nal 15mA

2.9 Software Implementation on the

Sensor

Arduino Uno programming can be operated using the

Arduino IDE. If Arduino can be likened to a brain,

then Arduino IDE is the mindset. Through this

application, we can adjust the Arduino Uno according

to the user's wishes. However, there are several steps

that we must do before we can use the program based

on what is written in it. The first step is writing the

program, then compiling the program and uploading

the program to Arduino Uno using the provided USB

cable.

Image of the implementation on the sensor can be

seen in Figure 4:

Figure 4: Arduino IDE application.

2.10 Testing Instrument

Data obtained through the SW-420 vibration sensor

will be processed in a microcontroller using fuzzy

logic methods. Thus the received vibration

classification degree will be generated. Furthermore,

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546

based on the results of data processing on fuzzy logic

classification of vibration levels is obtained.

Figure 9 shows the appearance of the application

using the built fuzzy inference system. In the

application display, the SW-420 sensor automatically

sends data to the Arduino Uno microcontroller when

it detects a vibration. Figure 5 shows the application

test display.

Figure 5: Display of Application Testing.

2.11.1 Input Variable Data Analog

The analog data variable provides information about

the average value received from the fifteen incoming

data from the SW-420 vibration sensor to the Arduino

Uno microcontroller. This data is used to determine

the number of detected vibration values so that it can

facilitate the classification to determine the degree of

vibration detected. The input data can be seen in

Figure 6:

Figure 6: Display of Analog Data Variable Input.

2.11.2 Input for Content Data Variable

The data content variable is a variable that functions

to accommodate the amount of data received from the

SW-420 vibration sensor. Therefore, every fifteen

data entry can be determined how many values

contain vibration values and what values do not detect

these vibrations. This makes classifications for

determining vibration levels easier to create. The

display of the data content variables can be seen in

Figure 7.

Figure 7: Display of Filled Data Variable Input.

2.11.3 Identify Set Value

Before using the fuzzy logic method, determining the

value of the data to be processed must be calculated

first by determining the value of each set. The image

of the analog data set input value is shown in Figure

Figure 8: Value of Analog Data Set.

Data from the SW-420 vibration sensor will

automatically enter the microcontroller and pass

through the Arduino IDE software in the Serial

Monitor section. We can see the incoming data

directly when one by one the data is processed and

calculated until the required amount of data is

reached. Then the program will determine the average

value of the data. Based on this data, the value is then

determined into which category set.

2.11.4 Final Classification

The final step is taken to convert the fuzzy data into

easy-to-understand data, in this case it means

displaying the final classification of vibrations that

have been received and processed by the Arduino

Uno microcontroller. The classification results can be

in the form of very weak, weak, moderate, strong, and

very strong vibration statements. Figure 9 shows a

display of final vibration classification results.

Analysis of the Sw-420 Vibration Sensor Performance on Vibration Tools by using a Fuzzy Logic Method

547

Figure 9: Final Classification.

2.11.5 Fuzzy Logic Method Analysis

In calculating fuzzification manually, 251 analog

input data and 7 content data can be taken as an

example.

Figure 10: Analog Data Membership.

As shown in Figure 11, the analog data value 251

is included in the low (lower curve) and medium

(upper curve) set. The membership degree value can

be written as follows:

Low set (down curve)

µ [x]= 0; x ≤ a or x ≥ d

(x-a) / (b-a); a < x ≤ b

1; b < x ≤ c

(d-x) / (d-c); c < x < d

µ [x] = (d-x) / (d-c); c < x < d

= (300-251)/ (300-250);

= 49 / 50

= 0,98

Moderate set (up curve)

µ [x] = 0; x ≤ a or x ≥ d

(x-a) / (b-a); a < x ≤ b

1; b < x ≤ c

(d-x) / (d-c); c < x < d

µ [x] = (x-a) / (b-a); a < x ≤ b

= (251-250) / (300-250);

= 1 / 50

= 0,02

Thus the set of µ analog data values obtained is

(0.98, 0.02, 0) or µ low [251] = 0.98, µ is medium

[251] = 0.02.

Figure 11: Content Data Membership.

It can be seen in Figure 12 that the value of 7 is on

a medium curve (peak), so the degree value can be

written as follows:

µ [y] = 0; x ≤ a or x ≥ d

(x-a) / (b-a); a < x ≤ b

1; b < x ≤ c

(d-x) / (d-c); c < x < d

Since the value 7 is at the vertex of the moderate

set, the formula is that x is less than b and x is less

than c, so the y value is 1.

µ [x] = 1 ; b < x ≤ c

µ [x] = 1.

So the set of µ is obtained (0,1,0). After the µ of the

two inputs is obtained, the next step is entering into

rule evaluation with the minimum comparison

method. The minimum comparison is obtained as

follows:

µ (x ∩ y) (1) = min [µ x [Low] ∩ µ y [Moderate]]

= min (0,98 ; 1)

= 0,98 (Low)

µ (x∩ y) (2) = min [µ x [Moderate] ∩ µ y [Sedang]

= min (0,02 ; 1)

= 0,02 (Low)

3 RESULTS AND DISCUSSIONS

After the minimum comparison implications have

been obtained, the final step is defuzification using

the center of single-ton method.

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548

Table 2: Rule Evaluation Fuzzy.

Amount

of Data

Content

Analog

Data

Value

Little

(0)

Average

(1)

Multitude

(0)

Low

(0,98)

Average

(0)

Multitude

(0.98)

Multitude

(0)

Moderate

(0,02)

Little

(0)

Little

(0.02)

Multitude

(0)

High

(0)

Little

(0)

Slight

(0)

Average

(0)

decision index = (0.98x8) + (0.02x18) / (0.98+0.02)

= (7.84 + 0.36) / (1)

= (8.2 / 1)

= 8.2 (Measly)

From the results of the theory that has been done,

and is referred to in the vibration level classification

table, the vibration level status is said to be in the very

small category.

Following are the results of testing the vibration

level with digital data without using the fuzzy logic

method:

Table 3: Test Results with Vibration Level Digital Data

Without Fuzzy Logic Method.

Sensor condition Information

No vibration detected

Vibration Detected

Based on the data obtained above, it can be

concluded that by using digital input data, the data

that can be generated by the sensor is only the

presence or absence of a detected vibration and does

not at all describe and produce a large level of

vibration received by the sensor.

Following are the results of testing the vibration

level with analog data without using the fuzzy logic

method:

Table 4: Vibration Level Test Results with Analog Data

Without Fuzzy Logic Method.

Data Mean Value Classification Results

0-299 Very weak

300-349 Weak

350-599 Medium

600-649 Stromg

650-700 Very strong

Based on the research that has been carried out to

build an algorithm with the support of the fuzzy logic

method, the data generated by the SW-420 vibration

sensor can produce a better classification than without

the fuzzy logic method. By adding the input data used

as the determining variable for vibration

classification, it can be said that digital and analog

data can be utilized properly with maximum work.

Previously, digital data was only able to receive

binary data, namely 0 and 1, which were received by

the sensor so that the sensor could only produce 2

classifications, namely vibration detection and

vibration detection without vibration magnitude

classification. Whereas the previous analog data was

able to receive a vibration level with values ranging

from 0-> 700. However, classification by only

utilizing a range of vibration values has drawbacks

such as a difference of 1 certain value in a condition

can affect the results of the classification of vibrations

which are considered unfair. For example 1-99 is a

small vibration and 100-200 is a moderate vibration.

The distance between 99 and 100 is only 1 but even

so the vibration will result in a different classification,

this is considered ineffective and can cause

countermeasures error depending on the type of

vibration sensor system applied. With the fuzzy logic

method, the vibration classification can be done better

and reduce the risk of data errors, so that the cause

and effect that will be carried out from the vibration

will be more targeted and effective.

4 CONCLUSIONS

Based on the research that has been done, several

conclusions are described as follows:

 Fuzzy logic method can be used for

classification needs of data taken from the SW-

420 vibration sensor using the Arduino Uno

microcontroller.

Analysis of the Sw-420 Vibration Sensor Performance on Vibration Tools by using a Fuzzy Logic Method

549

 The use of delay on the sensor can result in data

not being successfully received because the

sensor may detect data while it is still in the

delay process.

 With the fuzzy logic method, the resulting data

will be more accurate and can take into account

the many variables that determine the results of

a data.

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