Bearing Fault Detection and Classification Using Artificial Neural Networks

Author

Harnak Singh, California Polytechnic State University, San Luis ObispoFollow

DOI: https://doi.org/10.15368/theses.2022.64
Available at: https://digitalcommons.calpoly.edu/theses/2494

Date of Award

6-2022

Degree Name

MS in Electrical Engineering

Department/Program

Electrical Engineering

College

College of Engineering

Advisor

Xiao-Hua (Helen) Yu

Advisor Department

Electrical Engineering

Advisor College

College of Engineering

Abstract

Bearings are the essential components of modern rotating machines. Bearing faults can cause severe machine damages or even breakdowns.

In recent years, artificial intelligence and deep learning have been successfully applied to fault detection. In this thesis, convolutional neural networks (CNN) are employed for bearing fault detection and classification. Computer simulations results demonstrate that the CNN based approach is advantageous over the conventional regression model, with an overall accuracy of 99.5%.