IoT Application for Monitoring and Storage of Temperature History

in Electric Motors

Jairovan Denis de Paiva

, Carlos Roberto da Silveira Junior

and

Arquimedes Lopes da Silva

Saneago, Goiânia, Goiás, Brazil

Instituto Federal de Goiás, Goiânia, Goiás, Brazil

Keywords: IoT, Cloud Computing, Temperature Sensors, Temperature Measurement, Condition Monitoring, Induction

Motors.

Abstract: The three-phase induction motor is the most used type of motor. It is estimated that more than 90% of the

mechanical energy used in industry is supplied by three-phase induction motors. Therefore, an early and

unexpected failure of an electric motor is quite costly to the industry. This paper aims to present the

development of an IoT (Internet of Things) application for monitoring and storing the operating temperature

history of three-phase electric motors through a wireless sensor network. Real-time temperature values, peak

temperature values, tables and graphics of internal engine temperatures are displayed from web pages. Two

1200 HP motors were monitored. The temperatures were obtained through PT100 transducers installed in the

motor windings and the ambient temperature read by a digital sensor. The data read by the sensors is kept in

a database in the clouds, in order to generate relevant information to support the maintenance management of

these assets. Part of the application processing is performed in the clouds, such as the parameterization of the

microcontroller program and sending notifications via email, for cases of reading failure, communication

failure and high temperature alert. The results demonstrate the applicability and functionality of the

application in an industrial environment, allowing the identification of various engine behaviors over time.

1 INTRODUCTION

It is estimated that one third of maintenance costs are

wasted as a result of unnecessary or incorrectly

performed maintenance (MOBLEY, 2002, p.1).

These unnecessary maintenance often occur within

the scope of preventive maintenance, as their

management is based on time intervals defined by

statistical trends, which often do not reflect the actual

operational condition of the equipment. Predictive

maintenance arises to solve this problem, as

interventions are based on the condition of the

equipment, rather than the operating time. The

operational condition of the equipment is obtained

through regular monitoring of quantities such as

temperature, vibration, among others.

https://orcid.org/0000-0003-2912-9572

https://orcid.org/0000-0003-2891-929X

https://orcid.org/0000-0003-3202-1036

Detective maintenance is an evolution of

predictive maintenance, has more automation

features and uses intelligent electronic devices. It’s

based on systematic measurements of items that may

have hidden failures, where the loss of function

cannot be perceived by the operator and maintainer

(SEIXAS, 2011).

A large part of the anomalies observed in electric

motors are linked to the increase in operating

temperature, whether it's the cause, or the

consequence, of this temperature rise. Therefore,

monitoring the temperature of an engine in real time,

and maintaining a database with the temperature

history, in a structured way to generate relevant

information, provides ways to manage the

maintenance of these equipment more efficiently and

reliably. The measurement history can support studies

Denis de Paiva, J., Silveira Junior, C. and Lopes da Silva, A.

IoT Application for Monitoring and Storage of Temperature History in Electric Motors.

DOI: 10.5220/0010818300003118

In Proceedings of the 11th International Conference on Sensor Networks (SENSORNETS 2022), pages 121-128

ISBN: 978-989-758-551-7; ISSN: 2184-4380

121

of failures and possible errors in the design,

installation or operation of equipment.

The objective of this work is to develop an IoT

application for monitoring and storing the

temperature history of electric motors, using wireless

sensors connected to the internet and services

available in the cloud, in order to generate

information to support the management of the

maintenance of these assets. To carry out this

monitoring, a system with a microcontroller will be

used, which takes readings from PT100 sensors

installed in electric motors and sends these readings

to a database in the clouds. A WEB interface is used

for interaction with the user, allowing the user to

access this data, configure system parameters, receive

notifications and use the temperature history to

generate relevant information for maintenance

management.

2 OVERVIEW

2.1 Maintenance on Electric Motors

In short, maintenance actions can be divided into

three main types: corrective, preventive and

predictive. Corrective maintenance, the most costly

type, is carried out after a failure occurs, where the

equipment stops (NBR 5462, 1994). Preventive

maintenance is a type of time-based maintenance. It

must be carried out on predetermined dates, with the

aim of reducing the probability of failures

ALMEIDA, 2013). Despite having lower costs, it also

generates costs that could be avoided (MOBLEY,

2002, p.4).

Predictive maintenance seeks to obtain the actual

operating conditions of the machine. For this, it uses

specific equipment for monitoring phenomena such

as temperature, vibration, noise, etc. (ALMEIDA,

2013). The results of these inspections determine the

ideal time for intervention in the equipment. With the

evolution of embedded systems and industrial

networks, a new term appears in the area of

maintenance, detective maintenance. It differs from

predictive maintenance, by continuous monitoring

and the use of intelligent electronic devices

(PAULINO, 2014), with increased reliability

according to the level of the implemented system, in

addition to the possibility of storing the history of

equipment variables.

2.2 Effect of Temperature Rise on

Electric Motors

Copper losses are the major heat source of the

machine and are directly proportional to the load to

which the equipment is subjected. They occur due to

the joule effect on the resistive element of the

machine winding. Core losses or iron losses are due

to eddy currents and due to the hysteresis effect

(ALMEIDA, 2013). Harmonic currents and phase

voltage imbalances also cause temperature rise.

Delayed starts, due to loads with very high

resistant torque and successive starts also increase the

temperature of the equipment, as the starting current

reaches peaks of up to eight times the rated current.

In applications driven by frequency inverters, it

should be noted that when the motor speed is reduced,

the air flow produced by the fan coupled to the motor

shaft is reduced in the same proportion, which may

result in an increase in the temperature of the

equipment.

High temperature is the main villain of the

insulating material. The life of the insulation will be

reduced by half for each 10 °C increase in

temperature (GILL, 2009, p.9). In case of sudden

temperature rises in a short period of time, a failure

may occur due to material melting, causing an

immediate failure. On the other hand, temperatures

above the limit of the insulating class, but well below

the melting point, can for a long term cause internal

chemical effects, which make the material look more

dry, brittle, with micro-cracks, which causes

premature aging and degradation of insulation. With

the aging of the insulation, there are partial

discharges, which cause the progressive deterioration

of the insulating materials, leading to a total electrical

failure (TOLIYAT et al., 2013, p.11-12).

By monitoring and maintaining the temperature

history of the windings, it is possible to determine if

the winding is at risk of thermal deterioration and

degradation of the insulating material. In addition, the

finding of an increase in temperature under the same

operating conditions (load, ambient temperature and

voltage) may be indicative of failure or degradation

of the cooling and heat dissipation system

(TOLIYAT, et al. 2013, p.13) .

2.3 Wireless and IoT Sensor Network

Advances in technology, such as large-scale

integration, micro-electromechanical systems and

wireless communications, contribute to the feasibility

of implementing distributed sensor systems. When

many sensors cooperatively monitor large physical

SENSORNETS 2022 - 11th International Conference on Sensor Networks

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environments, they form a wireless sensor network.

A wireless sensor has, in addition to the transducer

component, processing, communication and storage

resources (DARGIE & POELLABAUER, 2010, p.7).

The wireless sensor network has the advantage of not

requiring cabling infrastructure, in addition to being

easily expandable and reconfigurable.

The wireless sensor network will be able to use

the internet infrastructure to interconnect its nodes

and use storage and processing services in the cloud.

In this context, the concept of IoT is entered. The term

has several definitions, however there is in common,

among the understanding of several authors, the idea

of being an environment of physical objects

interconnected through the internet, through small

sensors and actuators, introducing functional

solutions in everyday processes (MAGRANI, 2018,

p.20).

Large manufacturers such as WEG and ABB

provide applications for remote temperature

monitoring through wireless sensors. However, the

cost is still high for many cases. These applications

have a commercial nature, and their system and

source code are inaccessible and unalterable by users.

This is a major disadvantage, as it does not allow

integration to systems already implemented,

adaptation to user needs and the natural evolution of

these needs.

2.4 Related Works

Fabricio (2018) developed an application for

monitoring equipment on a production line, through

monitoring the consumption of electrical currents, in

order to detect operational deviations that could lead

to failures. The system uses an intermediate

concentrator node between the sensors and the

database, which is hosted on a personal computer. It

uses an IoT application to visualize the data in textual

and graphical form and send notification in the event

of operational deviations, with the history being

stored in the database to assist in the maintenance of

these equipment.

Pedotti's (2019) work presents a low-cost device,

which aims to diagnose failures through continuous

monitoring of vibration in rotating machines. The

ESP32 development board and WiFi communication

were used. Data transfer is done through the MQTT

protocol to a cloud computing platform, for the

storage and display of results.

Muta’ Ali (2021) developed an IoT application for

monitoring water quality in large areas. The system

consists of two microcontrollers, one performs

variable readings on the water and sends it to the other

through a long-range network. The second

microcontroller works as a gateway for connecting to

the internet, as it uploads the data to a cloud server.

This application uses the Google spreadsheet

application as its database. The user interface is

accessed through a WEB page. In the same line of

development, Kavitha and Vallikannu (2019)

developed a pollution control system, by monitoring

the level of gas or fuel by intelligent sensors in an

industry. This monitoring is carried out by a network

of wireless sensors, which detect gas leaks and their

location. Sensor data is also sent to the Google

spreadsheet.

3 MATERIALS AND METHODS

The project presented the following steps:

bibliographic study on the causes and effects of high

temperature in electric motors and on the use of

monitoring this temperature to help manage the

maintenance of these equipment; bibliographic study

on the use of wireless sensor networks and the use of

IoT in monitoring electrical equipment; survey of

application requirements; assembly of the electronic

circuit and development of the microcontroller and

WEB part software; tests and fixes; device

installation and data monitoring, to extract

information about the operating condition of the

equipment.

Figure 1 shows the architecture and functional

layers of the developed system. A microcontroller

performs temperature sensor readings in the engine

and the external environment, and after pre-

processing it sends this data over a wifi network to a

database hosted in a spreadsheet. This submission is

done through the forms feature, by an HTTP request.

The spreadsheet performs data processing in the

cloud and extracts relevant information, which feeds

WEB pages, which can be accessed by users.

Figure 1: System architecture and his functional layers.

The device was installed on two engines. The

monitored motors have a mechanical power of 1,200

CV, fed at medium voltage, for 2,300 V, and have

Class F insulation, which supports temperatures,

IoT Application for Monitoring and Storage of Temperature History in Electric Motors

123

without compromising their useful life, of up to 140

°C. These motors are used to drive centrifugal pumps.

In the physical device, the following were used:

ESP32 and ESP8266 development boards, the MAX

31865 resistance digital converter, the DHT22

temperature and humidity sensors, and LEDs for

signaling. To program the development board the

IDE (Integrated Development Environment) of

Arduino was used. For the development of the WEB

application, the Google Spreadsheet service and

Google Apps Scripts were used. For monitoring the

temperature of electric motors, in the way it was

conceived, the PT100 is the most suitable sensor.

Mainly due to its accuracy and greater immunity to

electrical noise.

ESP32 is a low-cost, power-consuming

development board with built-in Wi-Fi and Bluetooth

capabilities. Its use is very suitable for IoT project

solutions, due to the integration of components in a

single module (MAIER, SHARP, VAGAPOV,

2017). As a digital resistance converter, the MAX

31865 was used, optimized for thermoresistance

(PT100 and PT1000), with a resolution of 0.03125

°C, precision of 0.5 °C and an interface compatible

with SPI (serial peripheral interface) (MAXIM

INTEGRATED, 2015).

For hosting the data in the clouds, Google Sheets

was used, with storage and processing in the cloud.

For the development of the user notifications

application, Google Apps Scripts was used, a cloud

scripting language based on the JavaScript language,

which provides means for automating tasks, creating

functions, applications and integrating google

spreadsheets with other services from WEB and the

development of graphical interfaces to be used in

WEB applications (MAGUIRE, 2016, p.2-3).

4 RESULTS

For the electronic circuit, two temperature readers

were developed, one using the ESP32, which has

more features and the other using the ESP8266 which,

despite its lower performance, met the application

requirements and has a lower cost, being more

accessible for some applications. The electronic

circuit that uses the ESP32, was assembled as shown

in Figure 2, the other was assembled in a similar way,

changing only the input and output pins. The system

uses 3 resistance digital converters to read the PT100

temperature. The communication between the

microcontroller and the resistance digital converters

uses the SPI - Serial Peripheral Interface protocol,

which uses 3 shared pins for control, and one more

pin per device for device selection. SPI is a high-

speed full-duplex synchronous serial bus, with

Master/Slave control (Master/Slave) (DARGIE &

POELLABAUER, 2010, p.58).

Figure 2: Assembly of the electronic circuit. Where is

shown: (a) power supply; (b) outdoor temperature and

humidity sensor; (c) ESP32 controller; (d) MAX 31865

digital converter.

As the heating of an electric motor can be related

to the rise in the ambient temperature, and the ambient

temperature is also influenced by the engine

temperature, a digital ambient temperature and

humidity sensor, the DHT22, was used. Two LEDs

(Light Emitting Diodes) were used for signaling. The

blue LED flashes when communication is successful,

the red LED flashes when there is a failure.

There are several ways to edit the spreadsheet, by

an external system. The simplest is using Google

Forms, which is a form that can be linked to a

spreadsheet. In this way, it is possible to feed the

spreadsheet by sending responses through this form,

without the need for authentication. Another

advantage of using Forms is that it fills in the date and

time automatically when sending it, eliminating the

use of RTC (Real Time Clock) in hardware.

Clients and servers communicate through the

HTTP (Hypertext Transfer Protocol), which defines

how clients request files from servers and how they

transfer them to clients. An HTTP request message

has a header and an entity body. Among the methods

used by HTTP requests, the most common are GET

and POST. In the POST method the entity body will

contain the data typed in the fields of a form, for

example. In the GET method, these data are contained

in the requested URL itself and the entity's body is

sent empty.

To compress the data volume without losing

information, a conditional was inserted in the

program, so that the device sends temperatures only

when the temperature varies by an amount greater

than a predefined value. This reference value is a

program parameter that, along with other parameters,

can be defined by the user in the spreadsheet itself, in

an exclusive tab for configuring the parameters used

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by the microcontroller program, and by the WEB

application.

Unlike data submission, which uses the form

feature, spreadsheet reading requires API key

activation and use of authenticators. In the simplest

form, leaving the spreadsheet as public for reading, it

is only necessary to activate the API key, a simple

process that can be performed on Google's API

management platform.

The other parameters were used to define the

number of readings of the temperature converter to

calculate the temperature average, the number of

program cycles for updating the parameters and

finally the maximum number of readings without

sending data, where the device will send the data,

even not satisfying the temperature variation

condition, in order to enable the detection of failures.

For the web application, there are the temperature

limit parameters for notification, the maximum time

without receiving data for failure notification and the

registration of emails to receive these notifications.

In this way, the user can adapt the parameter

values according to the equipment to be monitored

and its working condition, in addition to taking into

account the number of sensors used. Using a

minimum variance of 0.5°C for submission, and a

maximum number of reads without submission equal

to 50, the number of submissions reduced to an

average of one submission every five minutes. Thus,

the same spreadsheet will support temperature

storage for a period exceeding 8 years.

The user also has the possibility to allow this

parameterization to be automatic, which determines

the best value for the parameters according to the

condition in which the equipment is found. Some

equipment is off for a long time, a situation in which

its temperature will be well below its working

temperature, so monitoring is not relevant. The graph

in Figure 3 shows an example of a device that was

turned off for more than 12 hours. It would not be

efficient, in terms of space occupation in the

spreadsheet, to maintain the same sending rate during

this period.

Figure 3: Motor 1's temperature graph. Points that

demarcate the period in which the engine remained off is

highlighted.

This way a script runs on the server, every time

temperature data is received. It searches within a table

with predefined values, table (d) in Figure 4, for a

group of parameters more suitable for the condition

the equipment is in, according to table (a) in Figure 4.

Automatic choice of the parameter group, takes into

account the temperature range in which the

equipment is located and the direction of variation, as

explained by the red rectangle markings in table (b)

of Figure 4. The selected parameter group is inserted

in the table (c) of Figure 4 for reading the

microcontroller. During the tests, at times of greatest

temperature variation, which occurred after the

equipment was turned on, the sending rate was

approximately 1 shipment every 30 seconds. On the

other hand, at times of thermal equilibrium, which

occurred most of the time, this send rate dropped to 1

send every 12 minutes on average.



Figure 4: Spreadsheet used for automatic parameterization:

(a) table of last readings; (b) table of average and direction

of temperature variation; (c) table of selected parameters

and (d) parameter groups table.

The same script that updates the parameters,

analyzes the data received, and in cases where the

temperature exceeds predefined values by the user or

when there is a failure in the sensors, it sends

notifications via e-mail to registered users. Another

script with a time-based execution trigger, different

from the first one that has an event-based trigger, is

executed on the server every pre-defined time period.

This script monitors the past time interval of the last

record and compares it with a predefined value. If it

exceeds this limit, it notifies, via e-mail, the user of a

possible communication failure.

Finally, the interface for the visualization of data

by users was developed. All information generated by

the data is available on WEB pages. The WEB pages

are multiplatform, that is, they can be accessed by

different devices, such as computers and

smartphones. The great advantage is that the user

does not need special applications to view the

IoT Application for Monitoring and Storage of Temperature History in Electric Motors

125

information, only an internet browser, which is

available on any device with internet access.

In the developed system, a kind of supervisory

was created in a spreadsheet tab, which displays the

latest readings and the graph of the last 24 hours, as

can be seen in Figure 5. Screens a and b in Figure 5

present the application's home page , where it shows

the temperatures of the last reading of each of the

monitored equipment and the temperature graph of

the last 24 hours.



Figure 5: Application screen viewed by a browser on an

Android device: (a) Application Home page, shows latest

sensor readings and graphs for the last 24 hours and (b)

exclusive page to access Motor 1's data.

On screen (b), there is the Engine 1 detail page,

which has hyperlinks to access more detailed

information about the operation of this engine, such

as tables and graphs of the last 24 hours, or the last 60

minutes and tables with peak temperature values

reached in each sensor of the equipment. Figure 6

shows a temperature graph within the engine 1 detail

pages.

Figure 6: Graphs for the last 24 hours.

During monitoring the equipment, Motor 1 had an

average winding temperature of 104°C and motor 2

of 111.6°C. Regarding the peak values, the highest

temperature recorded in engine 1 was 112.27°C and

in engine 2, 125.9°C. As the monitored equipment has

insulation class F, which withstand up to 140°

degrees, it is concluded that they are working with

adequate temperatures, and with a certain clearance

to the class limit. This indicates that the loads are well

dimensioned and that the heat dissipation process is

taking place efficiently.

The ambient temperature sensor has been

installed, in a suitable plastic frame, on the sensor

connection box. The assembly was in exactly the

same location as the two engines. The ambient

temperature sensor was very close to the equipment

frame, in order to reflect the external temperature of

the motor when measuring the ambient temperature.

When comparing the average temperatures of the

three PT100s with the ambient temperature of the two

engines simultaneously, it was observed, as shown in

Figure 7, that Engine 1, which worked at a

temperature below Engine 2, generated more heat to

the environment, as the ambient temperature around

it was higher than that of Engine 2.



Figure 7: Comparison between ambient temperature and

internal temperature of both engines simultaneously.

This indicates that the heat transfer from Motor 1

is more efficient than from Motor 2. The heat

generated inside the motor must be dissipated to the

environment through the surface of the equipment.

Cooling is aided by the fan mounted on the motor

shaft. To reduce the internal temperature of an engine,

there must be a good heat transfer from the engine's

interior to the external surface (GONÇÁLEZ, 2007).

Therefore, Motor 1, by transferring more heat to the

environment, further reduces the internal

temperature.

When analyzing the effect of the operating state

of one engine on the temperature of the other, it was

found, as shown in the graph in Figure 8, that when

turning Motor 1 off, indicated by the left arrow in

Figure 8, Motor 2 raises its internal temperature. On

the other hand, when Motor 1 is turned on again,

indicated by the arrow on the right in Figure 8, Motor

2 reduces its internal temperature again. This

temperature variation in the motor is due to the load

variation. According to (ELETROBRÁS, 2005, p.

137), a pump associated with another in parallel will

always provide a lower flow rate than when it works

SENSORNETS 2022 - 11th International Conference on Sensor Networks

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in isolation. Therefore, the parallelism of the pumps

reduces the flow in the pump, which implies a

reduction in the power demanded from the motor.

Figure 8: Temperature variation in one engine due to the

state of the other engine.

5 CONCLUSIONS

Electric motors are fundamental to most industrial

processes. Keeping this equipment in operating

condition is essential to ensure the effectiveness and

efficiency of these processes. Continuous monitoring

combined with good maintenance management of

these equipment can guarantee both reliability and

cost reduction, by determining the most appropriate

time to carry out interventions on these machines.

Advances in wireless sensor networks make this

continuous monitoring possible. These solutions have

a lot of computing resources, and can autonomously

perform complex operations, such as sending data to

a database hosted on an internet server, used as a

means to connect these sensors. The internet can also

be used for remote monitoring, data storage and data

processing by a multitude of existing web

applications. With advances in IoT technology and

with the expected arrival of the 5G internet, the

internet tends to become an increasingly powerful

tool.

Bringing together the simplicity of the

methodology used, the accuracy of the data generated

and the quality of the information displayed to the

user were only possible due to the use of applications

already consolidated on the internet, which provide,

through the use of APIs, a simple way to integrate.

them to simple projects, in order to maximize the

results, at very low cost, or often, as in the case of this

project, free of charge. The results achieved met all

the requirements raised at the beginning of the

project.

The data generated showed that the two monitored

devices were operating at an adequate temperature,

and with a gap to the limit of their insulation class,

which shows that the loads are well dimensioned and

that heat dissipation is efficient. However, it was

possible to observe, through the graphic analysis, that

the heat dissipation of Motor 1 was more efficient,

due to its lower internal temperature and higher

external temperature.

The results achieved open the way for future

projects, where this monitoring can extend to other

variables, in addition to temperature, such as current

and vibration, allowing for the correlation between

the quantities and increasing the diagnostic power. It

will also allow for long-term monitoring, enabling

analysis of how temperature behaves as parts, such as

bearings, begin to degrade. The generated data can

also be used by data science projects to create

predictive models to be used in predictive and

detective maintenance.

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