Fault identification of ball bearings using Fast Walsh Hadamard Transform, LASSO feature selection, and Random forest classifier
To reveal the machinery health condition, time-frequency analysis is an effective tool when signals are non-stationary. To identify bearing faults, numerous techniques have been proposed by various researchers. However, little research focused on image processing-based texture feature extraction for...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
University of Belgrade - Faculty of Mechanical Engineering, Belgrade
2022-01-01
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| Series: | FME Transactions |
| Subjects: | |
| Online Access: | https://scindeks-clanci.ceon.rs/data/pdf/1451-2092/2022/1451-20922201202D.pdf |