Practical Investigation on Bearing Fault Diagnosis Using Massive Vibration Data and Artificial Neural Network

2019 ◽  
pp. 110-116
Author(s):  
Tarek Khoualdia ◽  
Abdelaziz Lakehal ◽  
Zoubir Chelli
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Diagnosis ◽  
Bearing Fault ◽  
Vibration Data ◽  
Bearing Fault Diagnosis ◽  
Artificial Neural ◽  
Practical Investigation

Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals

2015 ◽  
Vol 89 ◽  
pp. 16-27 ◽  
Author(s):  
Jaouher Ben Ali ◽  
Nader Fnaiech ◽  
Lotfi Saidi ◽  
Brigitte Chebel-Morello ◽  
Farhat Fnaiech
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Artificial Neural

scholarly journals A hybrid artificial neural network with Dempster-Shafer theory for automated bearing fault diagnosis

2016 ◽  
Vol 18 (7) ◽  
pp. 4409-4418 ◽  
Author(s):  
Kar Hoou Hui ◽  
Ching Sheng Ooi ◽  
Meng Hee Lim ◽  
Mohd Salman Leong
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Diagnosis ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Dempster Shafer Theory ◽  
Artificial Neural ◽  
Shafer Theory ◽  
Hybrid Artificial Neural Network

Bearing fault diagnosis employing Gabor and augmented architecture of convolutional neural network

2019 ◽  
Vol 13 (3) ◽  
pp. 5689-5702
Author(s):  
N. Fathiah Waziralilah ◽  
Aminudin Abu ◽  
M. H. Lim ◽  
Lee Kee Quen ◽  
Ahmed Elfakarany
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Image Representation ◽  
Gabor Transform ◽  
Spatial Awareness ◽  
Bearing Fault ◽  
Vibration Data ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Model

The vast impact on machinery that is rooted by bearing degradation thus pinpointing bearing fault diagnosis as indubitably very crucial. The research is innovated to diagnose the fault in bearing by implementing deep learning approach which is Convolutional Neural Network (CNN) that has superiority over image processing and pattern recognition. A novel model comprises of Gabor Transform and CNN is proposed whereby Gabor Transform is utilized in representing the raw vibration signals into its image representation. The CNN architecture is augmented for a better accuracy of the bearing fault diagnosis model. To date, the method combination has never been deployed in establishing fault diagnosis model. Plus, the usage of Gabor Transform in mechanical area especially in bearing fault diagnosis is meagrely reported. Scant researches in mechanical diagnosis are dedicated to work on the image representation of the vibration data whereas the CNN works better when fed by images input due to its unique strength of CNN in images processing and spatial awareness. At the end of the research, it is perceived that the proposed model comprises of Gabor Transform and CNN can diagnose the bearing faults with 100% accuracy and perform better than when CNN is fed with raw signals.

scholarly journals Bearing Fault Diagnosis Based on BP Neural Network and Transfer Learning

2021 ◽  
Vol 1881 (2) ◽  
pp. 022084
Author(s):  
Ningbo Zhang ◽  
Yawei Li ◽  
Xingbo Yang ◽  
Junhao Zhang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Transfer Learning ◽  
Bp Neural Network ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis
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