A Novel Supervised Filter Feature Selection Method Based on Gaussian Probability Density for Fault D...
A Novel Supervised Filter Feature Selection Method Based on Gaussian Probability Density for Fault Diagnosis of Permanent Magnet DC Motors
About this item
Full title
Author / Creator
Wang, Weihao , Lu, Lixin and Wei, Wang
Publisher
Switzerland: MDPI AG
Journal title
Language
English
Formats
Publication information
Publisher
Switzerland: MDPI AG
Subjects
More information
Scope and Contents
Contents
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 for fault diagnosis of PMDCMs. Many redundant fea...
Alternative Titles
Full title
A Novel Supervised Filter Feature Selection Method Based on Gaussian Probability Density for Fault Diagnosis of Permanent Magnet DC Motors
Authors, Artists and Contributors
Author / Creator
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_20af15dab4474eacb4270483ba8c4747
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_20af15dab4474eacb4270483ba8c4747
Other Identifiers
ISSN
1424-8220
E-ISSN
1424-8220
DOI
10.3390/s22197121
