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%.
","abstract_html":"<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>","abstract_has_math":false,"creators":["Singh, Harnak"],"institution":null,"degree_name":"MS in Electrical Engineering","degree_level":null,"degree_discipline":"Electrical Engineering","degree_department":null,"school":null,"contributors":["Xiao-Hua (Helen) Yu","Electrical Engineering","College of Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-06-01T07:00:00Z","date_published":"2022-06-01T07:00:00Z","updated_at":"2026-07-24T01:32:29Z","subjects":["Convolutional Neural Networks","Bearing Fault","Fault Detection","Other Electrical and Computer Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["10.15368/theses.2022.64"],"render_values":[{"text":"10.15368/theses.2022.64","href":"https://doi.org/10.15368/theses.2022.64","code":true}]}]},"links":{"outbound_url":"https://digitalcommons.calpoly.edu/theses/2494","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Xiao-Hua (Helen) Yu","Electrical Engineering","College of Engineering"]},{"key":"dc:creator","label":"Author","values":["Singh, Harnak"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2025-06-09T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS in Electrical Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Convolutional Neural Networks","Bearing Fault","Fault Detection","Other Electrical and Computer Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.calpoly.edu/theses/2494","10.15368/theses.2022.64"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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%.
"]},{"key":"dc:title","label":"Title","values":["Bearing Fault Detection and Classification Using Artificial Neural Networks"]}]}],"canonical_facts":{"dc:contributor":["Xiao-Hua (Helen) Yu","Electrical Engineering","College of Engineering"],"dc:creator":["Singh, Harnak"],"dc:date.available":["2025-06-09T07:00:00Z"],"dc:description.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%.
"],"dc:identifier":["https://digitalcommons.calpoly.edu/theses/2494","10.15368/theses.2022.64"],"dc:subject":["Convolutional Neural Networks","Bearing Fault","Fault Detection","Other Electrical and Computer Engineering"],"dc:title":["Bearing Fault Detection and Classification Using Artificial Neural Networks"],"thesis:degree_discipline":["Electrical Engineering"],"thesis:degree_name":["MS in Electrical Engineering"]},"updated_at":"2026-07-24T01:32:29Z"}