FAULT DIAGNOSIS OF BALL BEARING BASED ON ELCD PERMUTATION ENTROPY AND RVM: Fid-Bau Portal

Fachinformationsdienst BAUdigital

A platform for research: civil engineering, architecture and urbanism

FAULT DIAGNOSIS OF BALL BEARING BASED ON ELCD PERMUTATION ENTROPY AND RVM

WANG Xia / GE MingTao

Aiming at the no stationary characteristic of a gear fault vibration signal, it proposes a recognition method based on ELCD（Ensemble local Characteristic-scale decomposition） permutation entropy and RVM. First, the vibration signal was decomposed by ELCD, then a series of intrinsic scale components were obtained; Secondly, according to the kurtosis of ISCs, principal ISCs were selected, then, calculate the permutation entropy of principal ISCs and combined into a feature vector; Finally, the feature vector were input RVM classifier to train and test to identify the type of rolling bearing faults. Experimental results show that this method can effectively diagnosis four kinds of working condition, and the effect is better than local Characteristic-scale decomposition method.

Access

Page navigation

Document information

Export, share and cite

FAULT DIAGNOSIS OF BALL BEARING BASED ON ELCD PERMUTATION ENTROPY AND RVM

WANG Xia / GE MingTao

Aiming at the no stationary characteristic of a gear fault vibration signal, it proposes a recognition method based on ELCD（Ensemble local Characteristic-scale decomposition） permutation entropy and RVM. First, the vibration signal was decomposed by ELCD, then a series of intrinsic scale components were obtained; Secondly, according to the kurtosis of ISCs, principal ISCs were selected, then, calculate the permutation entropy of principal ISCs and combined into a feature vector; Finally, the feature vector were input RVM classifier to train and test to identify the type of rolling bearing faults. Experimental results show that this method can effectively diagnosis four kinds of working condition, and the effect is better than local Characteristic-scale decomposition method.

FAULT DIAGNOSIS OF BALL BEARING BASED ON ELCD PERMUTATION ENTROPY AND RVM

WANG Xia / GE MingTao

Aiming at the no stationary characteristic of a gear fault vibration signal, it proposes a recognition method based on ELCD（Ensemble local Characteristic-scale decomposition） permutation entropy and RVM. First, the vibration signal was decomposed by ELCD, then a series of intrinsic scale components were obtained; Secondly, according to the kurtosis of ISCs, principal ISCs were selected, then, calculate the permutation entropy of principal ISCs and combined into a feature vector; Finally, the feature vector were input RVM classifier to train and test to identify the type of rolling bearing faults. Experimental results show that this method can effectively diagnosis four kinds of working condition, and the effect is better than local Characteristic-scale decomposition method.

Access

Page navigation

Document information

Export, share and cite

Document information

Title:

FAULT DIAGNOSIS OF BALL BEARING BASED ON ELCD PERMUTATION ENTROPY AND RVM

Contributors:

WANG Xia (author) / GE MingTao (author)

Published in:

Jixie qiangdu, Vol 41, Pp 290-295 (2019)

Publication date:

2019

ISSN:

Type of media:

Article (Journal)

Type of material:

Electronic Resource

Language:

Unknown

Keywords:

Rolling element bearing , Fault diagnosis , ELCD , RVM , Mechanical engineering and machinery , TJ1-1570 , Materials of engineering and construction. Mechanics of materials , TA401-492

Metadata by DOAJ is licensed under CC BY-SA 1.0

Similar titles

A rolling bearing fault diagnosis strategy based on improved multiscale permutation entropy and least squares SVM

Li, Y. / Zhang, W. / Xiong, Q. et al. | British Library Online Contents | 2017

Fault Monitoring and Diagnosis of Ball Bearing Based on Hilbert-Huang Transformation

Li, H. / Zheng, H. Q. / Tang, L. W. | British Library Online Contents | 2005

Control of mechatronics systems: Ball bearing fault diagnosis using machine learning techniques

Peng, Hsuan-Wen / Chiang, Pei-Ju | Tema Archive | 2011

RESEARCH ON BEARING FAULT DIAGNOSIS BASED ON CEEMDAN FUZZY ENTROPY AND CONVOLUTIONAL NEURAL NETWORK (MT)

XIAO JunQing / JIN JiangTao / LI Chun et al. | DOAJ | 2023

Free access

Ball Bearing Early Fault Detection Using Wavelet Analysis

Al-Khalidy, A. / Dragomir-Daescu, D. | British Library Conference Proceedings | 2003