An improved wrapper-based feature selection method for machinery fault diagnosis

• Main Authors: Hui, K. H., Ooi, C. S., Lim, M. H., Leong, M. S., Al-Obaidi, S. M.
• Format: Article
• Language: English
• Published: Public Library of Science 2017
• Subjects: QH301 Biology
• 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

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...