Feature-Based Bearing Fault Classification Using Taylor–Fourier Transform

Feature-Based Bearing Fault Classification Using Taylor–Fourier Transform

This paper proposes a feature-based methodology for early bearing fault detection and classification in induction motors through current signals using the digital Taylor–Fourier transform (DTFT) and statistical methods. The DTFT allows the application of narrow bandwidth digital filters located in t...

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Bibliographic Details
Main Authors: Gerardo Avalos-Almazan, Sarahi Aguayo-Tapia, Jose de Jesus Rangel-Magdaleno, Mario R. Arrieta-Paternina
Format: Article
Language:English
Published: MDPI AG 2023-10-01
Series:Machines
Subjects:
bearing damage
discrete-time Taylor–Fourier transform
fault classification
fault detection
induction machine
stator current
Online Access:https://www.mdpi.com/2075-1702/11/11/999

Internet

https://www.mdpi.com/2075-1702/11/11/999

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