Machine Learning-Based Prediction of Temperature Rise in Squirrel Cage Induction Motor (SCIM)

The three phase SCIM plays an important role in automobile and production industries due to its efficient and reliable operation. These are workhorse of all industries. They are low cost motors. Heavy continuous duty, load conditions, different duty cycle, wrong installation, environmental stress, temperature dissipation and manufacture imperfections can affect the life of machine. The temperature dissipation in SCIM is the main trouble for malfunction in it. Now a days, temperature prediction on induction motor is become more important in machine design companies. There are many conventional methods to predict the temperature rise in induction motor, but it requires more time and more knowledge on simulation software. In the current work the temperature rise on three phase SCIM is predicted using Machine Learning (ML) algorithms. The results of simulation is recorded, this temperature rise data is used in ML. The best ML algorithm for predicting temperature rise in SCIM is K-NN algorithm. In the present research work the cause of temperature rise is studied and the correct ML classification algorithm is initiated for temperature prediction in SCIM.