Bearing Fault Detection and Classification Using Artificial Neural Networks

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

Name thesis:degree_name

MS in Electrical Engineering

Discipline thesis:degree_discipline

Electrical Engineering

Year dc:date.available

2022

Author dc:creator

• Singh, Harnak

Contributors dc:contributor

• Xiao-Hua (Helen) Yu
• Electrical Engineering
• College of Engineering

• Convolutional Neural Networks
• Bearing Fault
• Fault Detection
• Other Electrical and Computer Engineering

Identifier

10.15368/theses.2022.64

OAI identifier oai:identifier

oai:digitalcommons.calpoly.edu:theses-4080