scholarly journals Dimension reduction graph‐based sparse subspace clustering for intelligent fault identification of rolling element bearings

2021 ◽  
Vol 1 (2) ◽  
pp. 207-219
Author(s):  
Le Zhao ◽  
Shaopu Yang ◽  
Yongqiang Liu
Keyword(s):  
Dimension Reduction ◽  
Subspace Clustering ◽  
Fault Identification ◽  
Rolling Element Bearings ◽  
Rolling Element ◽  
Sparse Subspace Clustering ◽  
Reduction Graph

scholarly journals Fault identification and severity assessment of rolling element bearings based on EMD and fast kurtogram

2016 ◽  
Vol 18 (6) ◽  
pp. 3668-3683 ◽  
Author(s):  
Sheng Fu ◽  
Lei Cheng ◽  
Hao Zheng ◽  
Yiming Huang ◽  
Yonggang Xu
Keyword(s):  
Fault Identification ◽  
Severity Assessment ◽  
Rolling Element Bearings ◽  
Rolling Element

scholarly journals Improved Fault Size Estimation Method for Rolling Element Bearings Based on Concatenation Dictionary

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 22710-22718 ◽  
Author(s):  
Lingli Cui ◽  
Xin Wang ◽  
Huaqing Wang ◽  
Na Wu
Keyword(s):  
Estimation Method ◽  
Rolling Element Bearings ◽  
Size Estimation ◽  
Rolling Element

Experimental investigation on time-domain features in the diagnosis of rolling element bearings by acoustic emission

2021 ◽  
pp. 107754632110161
Author(s):  
Aref Aasi ◽  
Ramtin Tabatabaei ◽  
Erfan Aasi ◽  
Seyed Mohammad Jafari
Keyword(s):  
Acoustic Emission ◽  
Condition Monitoring ◽  
Time Domain ◽  
Central Moment ◽  
Rolling Element Bearings ◽  
Test Rig ◽  
Acoustic Emission Sensor ◽  
Outer Race ◽  
Common Time ◽  
Rolling Element

Inspired by previous achievements, different time-domain features for diagnosis of rolling element bearings are investigated in this study. An experimental test rig is prepared for condition monitoring of angular contact bearing by using an acoustic emission sensor for this purpose. The acoustic emission signals are acquired from defective bearing, and the sensor takes signals from defects on the inner or outer race of the bearing. By studying the literature works, different domains of features are classified, and the most common time-domain features are selected for condition monitoring. The considered features are calculated for obtained signals with different loadings, speeds, and sizes of defects on the inner and outer race of the bearing. Our results indicate that the clearance, sixth central moment, impulse, kurtosis, and crest factors are appropriate features for diagnosis purposes. Moreover, our results show that the clearance factor for small defects and sixth central moment for large defects are promising for defect diagnosis on rolling element bearings.

Determination of Static Load Distributions from Elastic Contacts in Rolling Element Bearings

1971 ◽  
Vol 14 (4) ◽  
pp. 275-291 ◽  
Author(s):  
R. A. Goodelle ◽  
W. J. Derner ◽  
L. E. Root
Keyword(s):  
Static Load ◽  
Rolling Element Bearings ◽  
Load Distributions ◽  
Rolling Element
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