Helping world’s leading glass manufacturer to monitor equipment’s health and predict maintenance with IoT based equipment monitoring system.

1. IOBOT
iobot Technologies
Equipment monitoring system -Case Study
iobot Technologies
Innovating Life
www.iobot.in
SAFETY
FIRST

2. 2
iobot Technologies
IOBOT
Helping world’s leading glass manufacturer to monitor equipment’s health with IoT
based equipment monitoring system
Equipment monitoring system
As industrial operations continue to mature, customers in manufacturing, oil and gas, transportation,
energy and utilities are challenged with making sense of industry trends and determining how and why
they apply to their day-to-day activities. With IoT, industry is moving towards unplanned maintenance with
predictive analytics from calendar maintenance.
The fundamental block of the predictive maintenance is getting machine parameters like temperature,
power consumption, audio signature, oil level, vibrations etc. It is commonly observed that not all
machines support these sensors by default. The purpose of the EMS system is to measure these
parameters from machine externally and transmit the data to cloud for predictive analytics.

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iobot Technologies
IOBOT
Client
Client is a subsidiary of global conglomerate that manufactures and markets solar control glass, fire
resistant glass and other various types of float glasses in India. The products and solutions developed by
the client impact almost every aspect of our daily life. Today it is among the top one hundred industrial
groups worldwide and is a European or world leader in all of its areas of activity.
“Practical solutions for everyday living”: As a producer, processor and distributor of material (glass,
ceramics, plastics…), client transforms raw materials for use into advanced products for use in our
everyday lives.

4. IOBOT
iobot Technologies
 Monitor real time and historical data of various KPI
parameters of the machines like temperature at
different sections, audio signature, power
consumption, vibration of different section, access
control of machine console
 Sample the real time data with 10milliseconds
sampling rate
 Log the data locally on device as well as on cloud
 Build mobile app and web dashboard for the real
time data visualization.
 Scalable cloud app that could accommodate
hundreds of machines on the go
 Predict machine health based on the KPI parameters
 We wireless sensor nodes that could be
mounted externally on the machine to monitor
KPI parameters using Thermal cams, directional
MEMs microphone, vibration sensors etc
 The sensor nodes sample the data with
microseconds sampling rate and transmit it to
cloud over wifi
 With built in memory all the data got stored on
device as well.
 The health and failure model of the machines is
designed using data generated from machine
using data science algorithms
SolutionChallenges

5. 5
iobot Technologies
IOBOT
Results
Retrofitting Remote monitoring Maintenance Development Time
Wireless sensor nodes
are perfectly retrofitted
and mounted to existing
machines
All machine KPI’s could be
monitored remotely from
mobile app and web app
With data science and
machine learning
algorithms predictive
maintenance alerts
With ready to plug in
hardware units,
development time
reduced considerably