Integration of Machine Learning for an Induction Motor Health Monitoring by Image Capture and Classification Method

Induction motor is the most widely used resource at industries on a global level. It is used primarily at industries due to flexible operating conditions and its working performance. Various scientists are proposing different techniques to investigate the induction motor’shealth condition. This paper contains the most efficient technique among all others. Machine Learning (ML) has been used and adopted to learn about theinduction motor’s health conditionwhich is to be used in industries. In the computer vision approach that uses intrusive weed optimization and grey color founder matrices, the problematic circumstances are identified using temperature measurements. The details available uses an algorithm called the gray scale cross matrices to identify potential medical issues. The results so obtained gives the performance of the proposed method.