Predictive maintanace of single phase induction motor using data analysis

Our idea is to develop a portable devise which can attach with single phase induction motors. The device must be capable of extracting all important data of motor as: current consumption, operating voltages, RPM, Power consumption, power factor, temperature, vibration, and magnetic field. Aft

Project Title

Predictive maintanace of single phase induction motor using data analysis

Project Area of Specialization

Electrical/Electronic Engineering

Project Summary

Project Summary

Our idea is to develop a portable devise which can attach with single phase induction motors. The device must be capable of extracting all important data of motor as: current consumption, operating voltages, RPM, Power consumption, power factor, temperature, vibration, and magnetic field.

After extracting all vital data values from operating motor, the device will also be capable of upload this data on cloud. The uploaded data will analyze, and predictions of faults and maintenance of motor will be made on the bases of measured and analyzed data. We complete our project by taking support of ThingSpeak for MATLAB predictive algorithms designing and module of NODEMCU for internet of Thing.

Project Objectives

• Reduce the downtime of moto maintenance by pre-fault detection and predictive maintenance.

• Useful data must be uploaded on cloud so that it can be access from any where.

• We must develop two different algorithm one is mathematical model and second one is comparative model.

• At last, we must compare the efficiency of both models.

Project Implementation Method

Many predictive techniques are applied to these motors to reduce the number of unplanned outages. The most common techniques applied to fault detection in induction motors are vibration analysis, acoustical analysis, speed oscillations, partial discharges, circuit analysis, etc. Electrical Signature presents the comparative between vibration analysis and ESA (considering Motor

Current Signature Analysis (MCSA), Extended Park’s Vector Approach (EPVA) and Instantaneous Power Signature Analysis (IPSA)), showing which technique is more recommended to a specific kind of problem in a determined part of the rotating drive train. One can say that those techniques are complementary. Analysis (ESA) is calling more and more attention of industries.

Benefits of the Project

• Induction motor is one of the essential and most important  part of every industry.
• In industry it is very difficult to look after all the motors for its maintenance.
• The unpredictive failure of motor effect the entire production line and result are in the form of less production and waste of time.

Technical Details of Final Deliverable

• Research on induction motors and understand it faults, faults types and signs of faults.
• By understanding arriving fault signs, we will decide the sensors to be use in hardware.
• After deciding of sensors, we must design circuit, body of device, and learn how to interface sensor with NODEMCU.
• Develop the device in such a way that it can upload data on cloud of ThingSpeak sever through internet of Things.
• After the finishing hardware we are capable of extracting data from operating motors. Now at final stage we must do word on MTALB to design algorithms which are capable of analyze data and predict faults, maintenance, and remaining life of motors.

Final Deliverable of the Project

Hardware System

Core Industry

Manufacturing

Other Industries

Petroleum , Agriculture , Food

Core Technology

Internet of Things (IoT)

Other Technologies

Big Data

Sustainable Development Goals

Industry, Innovation and Infrastructure

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com