A novel feature extraction method for bearing fault classification with one dimensional ternary patterns-Reference-Cited by-同舟云学术

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A novel feature extraction method for bearing fault classification with one dimensional ternary patterns

Published:2020-05 Issue: Volume:100 Page:346-357
ISSN:0019-0578
Container-title:ISA Transactions
language:en
Short-container-title:ISA Transactions

Author:

Kuncan Melih,Kaplan Kaplan,Mi̇naz Mehmet Recep,Kaya Yılmaz,Ertunç H. Metin

Publisher

Elsevier BV

Subject

Applied Mathematics,Control and Systems Engineering,Electrical and Electronic Engineering,Computer Science Applications,Instrumentation

Reference46 articles.

1. Fault feature extraction using independent component analysis with reference and its application on fault diagnosis of rotating machinery;Yu;Neural Comput Appl,2015

2. Periodic feature-oriented adapted dictionary free OMP for rolling element bearing incipient fault diagnosis;Huang;Mech Syst Signal Process,2019

3. General normalized sparse filtering: A novel unsupervised learning method for rotating machinery fault diagnosis;Zhang;Mech Syst Signal Process,2019

4. Sparse representation based on parametric impulsive dictionary design for bearing fault diagnosis;Sun;Mech Syst Signal Process,2019

5. Order spectrogram visualization for rolling bearing fault detection under-speed variation conditions;Wang;Mech Syst Signal Process,2019

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