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%.
| Identifer | oai:union.ndltd.org:CALPOLY/oai:digitalcommons.calpoly.edu:theses-4080 |
| Date | 01 June 2022 |
| Creators | Singh, Harnak |
| Publisher | DigitalCommons@CalPoly |
| Source Sets | California Polytechnic State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Master's Theses |
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