A Novel Supervised Filter Feature Selection Method Based on Gaussian Probability Density for Fault Diagnosis of Permanent Magnet DC Motors

A Novel Supervised Filter Feature Selection Method Based on Gaussian Probability Density for Fault Diagnosis of Permanent Magnet DC Motors

For permanent magnet DC motors (PMDCMs), the amplitude of the current signals gradually decreases after the motor starts. In this work, the time domain features and time-frequency-domain features extracted from several successive segments of current signals make up a feature vector, which is adopted...

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Bibliographic Details
Main Authors: Weihao Wang, Lixin Lu, Wang Wei
Format: Article
Language:English
Published: MDPI AG 2022-09-01
Series:Sensors
Subjects:
feature selection
fault diagnosis
dimension reduction
machine learning
Online Access:https://www.mdpi.com/1424-8220/22/19/7121

Internet

https://www.mdpi.com/1424-8220/22/19/7121

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