scholarly journals Fault diagnosis and health management of bearings in rotating equipment based on vibration analysis – a review

2021 ◽  
Author(s):  
Adnan Althubaiti ◽  
Faris Elasha ◽  
Joao Amaral Teixeira
Keyword(s):  
Fault Diagnosis ◽  
Vibration Analysis ◽  
Health Management ◽  
Rotating Equipment

Power Transformer Fault Diagnosis Based on Vibration Correlation Analysis

2014 ◽  
Author(s):  
Kaixing Hong ◽  
Hai Huang
Keyword(s):  
Fault Diagnosis ◽  
Correlation Analysis ◽  
Fundamental Frequency ◽  
Vibration Analysis ◽  
Laboratory Tests ◽  
Power Transformer ◽  
Assessment Model ◽  
Vibration Method ◽  
Proposed Model ◽  
Transformer Fault

In this paper, a condition assessment model using vibration method is presented to diagnose winding structure conditions. The principle of the model is based on the vibration correlation. In the model, the fundamental frequency vibration analysis is used to separate the winding vibration from the tank vibration. Then, a health parameter is proposed through the vibration correlation analysis. During the laboratory tests, the model is validated on a test transformer, and manmade deformations are provoked in a special winding to compare the vibrations under different conditions. The results show that the proposed model has the ability to assess winding conditions.

scholarly journals Health Condition Estimation of Bearings with Multiple Faults by a Composite Learning-Based Approach

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4424
Author(s):  
Udeme Inyang ◽  
Ivan Petrunin ◽  
Ian Jennions
Keyword(s):  
Fault Diagnosis ◽  
Health Management ◽  
Network Models ◽  
Health Condition ◽  
Limited Attention ◽  
Neural Network Models ◽  
Multiple Fault ◽  
Multiple Faults ◽  
Multiple Fault Diagnosis ◽  
Non Gaussian

Bearings are critical components found in most rotating machinery; their health condition is of immense importance to many industries. The varied conditions and environments in which bearings operate make them prone to single and multiple faults. Widespread interest in the improvements of single fault diagnosis meant limited attention was spent on multiple fault diagnosis. However, multiple fault diagnosis poses extra challenges due to the submergence of the weak fault by the strong fault, presence of non-Gaussian noise, coupling of the frequency components, etc. A number of existing convolutional neural network models operate on a distinct feature that is not enough to assure reliable results in the presence of these challenges. In this paper, extended feature sets in three homogenous deep learning models are used for multiple fault diagnosis. This ensures a measure of diversity is introduced to the health management dataset to obtain complementary solutions from the models. The outputs of the models are fused through blending ensemble learning. Experiments using vibration datasets based on bearing multiple faults show an accuracy of 98.54%, with an improvement of 2.74% in the overall effectiveness over the single models. Compared with other technologies, the results show that this approach provides an improved generalized diagnostic capability.

Statistical fault diagnosis based on vibration analysis for gear test-bench under non-stationary conditions of speed and load

2012 ◽  
Vol 29 ◽  
pp. 436-446 ◽  
Author(s):  
Luisa F. Villa ◽  
Aníbal Reñones ◽  
Jose R. Perán ◽  
Luis J. de Miguel
Keyword(s):  
Fault Diagnosis ◽  
Vibration Analysis ◽  
Test Bench ◽  
Stationary Conditions

Fault diagnosis of railway roller bearing based on vibration analysis and information fusion

2012 ◽  
Vol 131 (4) ◽  
pp. 3307-3307 ◽  
Author(s):  
Bin Chen ◽  
Zhaoli Yan ◽  
Xiaobin Cheng ◽  
Wei Liu
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Vibration Analysis ◽  
Roller Bearing
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