IDENTIFICATION OF BEARING FAULT IN ROBOTIC PRECEPT USING SFFS

Authors

  • Dr. R. Menaka, B.P. Naveenya, S. Snekha, M. Yamuna

Keywords:

– Machine Learning, SFFS, Bearing

Abstract

The ultimate goal of the project is to find out the faulty bearing earlier by using machine learning. The faulty bearing also affects the other parts of machine such as high contact friction leads to heat results in fire. The wear and torn of faulty bearing cause the machine to consume excessive power due to load and affects the product quality. Although the depreciation of bearing affects the timing mechanism in a machine. The data for training, testing development of model are taken from Kaggle. The dataset from the Kaggle is pre- processed to remove noisy and to fill missing data by mean, median of the columns. After the pre- processing the data is split for training and testing. Initially the prediction is done by supervised learning using Support Vector Machine and compared with unsupervised learning by using Artificial Neural Network to achieve more accuracy.

Published

2022-06-28

How to Cite

Dr. R. Menaka, B.P. Naveenya, S. Snekha, M. Yamuna. (2022). IDENTIFICATION OF BEARING FAULT IN ROBOTIC PRECEPT USING SFFS. International Journal of Advanced Engineering Science and Information Technology, 10(6), 57–60. Retrieved from http://ijaesit.com/index.php/home/article/view/92