Novel Bearing Fault Diagnosis Using Gaussian Mixture Model-Based Fault Band Selection

Novel Bearing Fault Diagnosis Using Gaussian Mixture Model-Based Fault Band Selection

This paper proposes a Gaussian mixture model-based (GMM) bearing fault band selection (GMM-WBBS) method for signal processing. The proposed method benefits reliable feature extraction using fault frequency oriented Gaussian mixture model (GMM) window series. Selecting exclusively bearing fault frequ...

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Bibliographic Details
Main Authors: Andrei S. Maliuk, Alexander E. Prosvirin, Zahoor Ahmad, Cheol Hong Kim, Jong-Myon Kim
Format: Article
Language:English
Published: MDPI AG 2021-10-01
Series:Sensors
Subjects:
bearing
electric motor
fault diagnosis
feature extraction
feature selection
gaussian window
Online Access:https://www.mdpi.com/1424-8220/21/19/6579

Internet

https://www.mdpi.com/1424-8220/21/19/6579

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