Predictive Maintenance of Industrial Motor

Project Title

Predictive Maintenance of Industrial Motor

Project Area of Specialization Electrical/Electronic EngineeringProject Summary

According to research, there are more than 300 million electric motors worldwide. Most of them are used to drive diverse types of industrial machinery in a wide range of industrial areas (steel, paper, cement, petrochemical, and mining industries). However, their use also expands to other applications that play a capital role in the development of today’s societies, such as electric vehicles, traction systems, aerospace applications, and even robotics. In 2017, the global electric motor market size reached $96,967.9 million, which gives an idea of the significance of these machines in modern societies.
Our project is related to the predictive maintenance of motors on an industrial scale to solve the problem of unscheduled downtime and poor asset quality. Electric Motors need maintenance regularly to avoid failure and prolong their lifespan.
The physical measurement system must be converted into the electronic measurement remote monitoring system. In the remote monitoring system, a person could access the information far away from the monitoring device using IOT based system. If an electronic device without a remote monitoring system is fixed to the motor. It is not even better because we have to go for monitoring that will disturb the flow of production. 

Project Objectives

Our objective is to design a remote monitoring device that gives all the necessary parameters of a motor with the following properties to access different parameters required for a predictive maintenance schedule. Such as Temperature, Vibration, Power, and sound through this device.
With the help of predictive maintenance, we will be able to predict the next maintenance schedule of our electrical/industrial motor.

Project Implementation Method

Firstly, we will study the basic principles and working of different sensors. Such as vibration sensors, heat sensors, power sensors, etc. In the next phase, Data is collected from sensors for model training. For working on model training we should learn about python language to develop machine algorithms. we implement machine learning algorithms and train models on our data set Then we finalize and test all algorithms

Benefits of the Project

1 Minimizes unplanned downtime of mission-critical assets.

2 Reduces time spent on maintenance.

3 Increase the life expectancy of machines and equipment. Increased by 20%-40%.

4 Reduces machine breakdowns and unexpected failures.

5 Minimizes costs spent on labor, spare parts and equipment.

6 Reduces stock of spare parts due to increased service life of assets.

7 Improve safety throughout the workplace for the technicians and operators.

Technical Details of Final Deliverable

At the end we make a circuit or device that will analysis the whole data and give us details about the condition of motor at the monitoring unit using IOT base developed system 

Final Deliverable of the Project Hardware SystemCore Industry Energy Other Industries Others Core Technology Internet of Things (IoT)Other Technologies Big DataSustainable Development Goals Decent Work and Economic Growth, Industry, Innovation and InfrastructureRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	55000
Aruino UNO	Equipment	2	1500	3000
3 phase induction motor	Equipment	2	15000	30000
Other	Miscellaneous	2	5000	10000
heat sensor	Equipment	2	2000	4000
vibration sensor	Equipment	2	1000	2000
temperature sensor	Equipment	2	3000	6000