A hybrid artificial neural network with dempster-shafer theory for automated bearing fault diagnosis
Bearing fault diagnosis has a pivotal role in condition-based maintenance. Vibration spectra analysis has been proven to be the most efficient method for rotating machinery fault diagnosis. Vibration spectra can be analyzed by various signal processing tools (e.g. wavelet analysis, empirical mode de...
Saved in:
Main Authors: | Hui, Kar Hoou, Ooi, Ching Sheng, Lim, Meng Hee, Leong, M. Salman |
---|---|
Format: | Article |
Published: |
Vibromechanika
2016
|
Subjects: | |
Online Access: | http://eprints.utm.my/id/eprint/71318/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85027689418&doi=10.21595%2fjve.2016.17024&partnerID=40&md5=dc533e0c5e08eb3c1bafb6c073889320 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Dempster-shafer evidence theory for multi-bearing faults diagnosis
by: Kar, Hoou Hui, et al.
Published: (2017) -
Dempster-Shafer evidence theory for automated bearing fault diagnosis
by: Hui, K. H., et al.
Published: (2017) -
A hybrid method of support vector machine and Dempster-Shafer theory for automated bearing fault diagnosis
by: Lim, Meng Hee, et al.
Published: (2015) -
A hybrid artificial neural network with dempster-shafer theory for automated bearing fault diagnosis
by: Hui, K. H., et al.
Published: (2016) -
Integration of artificial intelligence into dempster shafer theory: a review on decision making in condition monitoring
by: Rosli, Muhammad Firdaus, et al.
Published: (2015)