An improved wrapper-based feature selection method for machinery fault diagnosis
A major issue of machinery fault diagnosis using vibration signals is that it is over-reliant on personnel knowledge and experience in interpreting the signal. Thus, machine learning has been adapted for machinery fault diagnosis. The quantity and quality of the input features, however, influence th...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science
2017
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/74838/1/SalahMahdi2017_AnImprovedWrapperbasedFeatureSelection.pdf http://eprints.utm.my/id/eprint/74838/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85038857642&doi=10.1371%2fjournal.pone.0189143&partnerID=40&md5=d604d511549609beb5ef7129729a158c |
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