Mulit-core architecture for real-time and energy-efficient bearing fault diagnosis

2014 ◽  
Author(s):  
Myeongsu Kang ◽  
Inkyu Jeong ◽  
Jong-Myon Kim
Keyword(s):  
Fault Diagnosis ◽  
Real Time ◽  
Energy Efficient ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Time And Energy

scholarly journals Research on rolling bearing fault diagnosis based on multi-dimensional feature extraction and evidence fusion theory

2019 ◽  
Vol 6 (2) ◽  
pp. 181488 ◽  
Author(s):  
Jingchao Li ◽  
Yulong Ying ◽  
Yuan Ren ◽  
Siyu Xu ◽  
Dongyuan Bi ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Real Time ◽  
Rolling Bearing ◽  
Diagnostic Methods ◽  
Bearing Fault ◽  
Feature Vectors ◽  
Bearing Fault Diagnosis ◽  
Fusion Theory

Rolling bearing failure is the main cause of failure of rotating machinery, and leads to huge economic losses. The demand of the technique on rolling bearing fault diagnosis in industrial applications is increasing. With the development of artificial intelligence, the procedure of rolling bearing fault diagnosis is more and more treated as a procedure of pattern recognition, and its effectiveness and reliability mainly depend on the selection of dominant characteristic vector of the fault features. In this paper, a novel diagnostic framework for rolling bearing faults based on multi-dimensional feature extraction and evidence fusion theory is proposed to fulfil the requirements for effective assessment of different fault types and severities with real-time computational performance. Firstly, a multi-dimensional feature extraction strategy on the basis of entropy characteristics, Holder coefficient characteristics and improved generalized box-counting dimension characteristics is executed for extracting health status feature vectors from vibration signals. And, secondly, a grey relation algorithm is used to calculate the basic belief assignments (BBAs) using the extracted feature vectors, and lastly, the BBAs are fused through the Yager algorithm for achieving bearing fault pattern recognition. The related experimental study has illustrated the proposed method can effectively and efficiently recognize various fault types and severities in comparison with the existing intelligent diagnostic methods based on a small number of training samples with good real-time performance, and may be used for online assessment.

scholarly journals Semi-Real-Time Bearing Fault Diagnosis Method Combined Image Method

2021 ◽  
Vol 33 (6) ◽  
pp. 895-903
Author(s):  
Pengzhi Wang ◽  
Mandun Zhang ◽  
Yahong Han ◽  
Xu Zhao ◽  
Zheng Wang
Keyword(s):  
Fault Diagnosis ◽  
Real Time ◽  
Image Method ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

The Non-Constant Speed Bearing Fault Diagnosis Based on Speed Measuring Method

2013 ◽  
Vol 760-762 ◽  
pp. 901-905
Author(s):  
Bin Wu ◽  
Shi Jun Wang ◽  
Ying Tang ◽  
Yue Gang Luo
Keyword(s):  
Fault Diagnosis ◽  
Real Time ◽  
Measuring Method ◽  
Measurement Methods ◽  
Constant Speed ◽  
Measuring Range ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Time Performance ◽  
Speed Measurement

This paper compares the existing speed measurement methods, discusses a speed measurement based on microcontroller used in non-constant speed bearing fault diagnosis that has the wide measuring range, high precision, good real-time performance.

Sign in / Sign up

Export Citation Format

Share Document