Morphological undecimated wavelet decomposition for fault diagnostics of rolling element bearings

2009 ◽  
Vol 320 (4-5) ◽  
pp. 1164-1177 ◽  
Author(s):  
Rujiang Hao ◽  
Fulei Chu
Keyword(s):  
Wavelet Decomposition ◽  
Rolling Element Bearings ◽  
Fault Diagnostics ◽  
Rolling Element ◽  
Undecimated Wavelet

Deep semi-supervised generative adversarial fault diagnostics of rolling element bearings

2019 ◽  
Vol 19 (2) ◽  
pp. 390-411 ◽  
Author(s):  
David Benjamin Verstraete ◽  
Enrique López Droguett ◽  
Viviana Meruane ◽  
Mohammad Modarres ◽  
Andrés Ferrada
Keyword(s):  
Multidimensional Data ◽  
Data Streaming ◽  
Rolling Element Bearings ◽  
Fault Diagnostics ◽  
Generative Adversarial Network ◽  
Vibration Data ◽  
Adversarial Network ◽  
Multidimensional Datasets ◽  
Rolling Element ◽  
Public Datasets

With the availability of cheaper multisensor suites, one has access to massive and multidimensional datasets that can and should be used for fault diagnosis. However, from a time, resource, engineering, and computational perspective, it is often cost prohibitive to label all the data streaming into a database in the context of big machinery data, that is, massive multidimensional data. Therefore, this article proposes both a fully unsupervised and a semi-supervised deep learning enabled generative adversarial network-based methodology for fault diagnostics. Two public datasets of vibration data from rolling element bearings are used to evaluate the performance of the proposed methodology for fault diagnostics. The results indicate that the proposed methodology is a promising approach for both unsupervised and semi-supervised fault diagnostics.

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.

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