scholarly journals Rolling Element Bearing Condition Monitoring Based on Vibration Analysis Using Statistical Parameters of Discrete Wavelet Coefficients and Neural Networks

2017 ◽  
Vol 7 (2) ◽  
pp. 61-69 ◽  
Author(s):  
Vahid Kazemi Golbaghi
Keyword(s):  
Neural Networks ◽  
Condition Monitoring ◽  
Vibration Analysis ◽  
Rolling Element Bearing ◽  
Discrete Wavelet ◽  
Wavelet Coefficients ◽  
Statistical Parameters ◽  
Bearing Condition Monitoring ◽  
Rolling Element ◽  
Element Bearing

scholarly journals Rolling Element Bearing Condition Monitoring using Filtered Acoustic Emission

2018 ◽  
Vol 8 (5) ◽  
pp. 3560
Author(s):  
Sudarsan Sahoo ◽  
J. K. Das ◽  
Bapi Debnath
Keyword(s):  
Condition Monitoring ◽  
Acoustic Signal ◽  
Rolling Element Bearing ◽  
Time Frequency ◽  
Second Stage ◽  
Acoustic Signature ◽  
Bearing Condition Monitoring ◽  
Rolling Element ◽  
Element Bearing ◽  
Two Stages

The defect present in the bearing of a rolling element may affect the performance of the rotating machinery and may reduce its efficiency. For this reason the condition monitoring of a rolling element bearing is very essential. So many measuring parameters are there to diagnose the fault in a rolling element bearing. Acoustic signature monitoring is one of them. Every rolling element bearing has its own acoustic signature when it is in healthy condition and when the bearing get defected then there is a change in its original acoustic signature. This change in acoustic signature can be monitored and analyzed to detect the fault present in the bearing. But the noise present in the acquired acoustic signal may affect the analysis. So the noisy acoustic signal must be filtered before the analysis. In this work the experiment is performed in two stages. In first stage the filtration of the acquired acoustic signal is done by employing the active noise cancellation (ANC) filtering techniques. In second stage the filtered signal is used for the further analysis. For the analysis initially the static analysis is done and then the frequency and the time-frequency analysis is done to diagnose the defect in the bearing. From all the three analysis the information about the defect present in the bearing is well detected.

Rolling Element Bearing Condition Monitoring and Diagnosis

2010 ◽  
Vol 34-35 ◽  
pp. 332-337
Author(s):  
Hui Bin Lin ◽  
Kang Ding
Keyword(s):  
Condition Monitoring ◽  
Rolling Element Bearing ◽  
Bearing Fault ◽  
Monitoring And Diagnosis ◽  
Bearing Condition Monitoring ◽  
Envelope Detection ◽  
Rolling Element ◽  
Machine Speed ◽  
Bearing Characteristic ◽  
The Impact

Bearing failure is one of the foremost causes of breakdown in rotating machinery. To date, Envelope detection is always used to identify faults occurring at the Bearing Characteristic Frequencies (BCF). However, because the impact vibration generated by a bearing fault has relatively low energy, it is often overwhelmed by background noise and difficult to identify. Combined the results of extensive experiments performed in a series of bearings with artificial damage, this research investigates the effect of many influencing factors, such as demodulation methods, sampling frequency, variable machine speed and the signals collected in different directions, on the effectiveness of demodulation and the implications for bearing fault detection. By understanding these effects, a more skillful application of the envelope detection in condition monitoring and diagnosis is achieved.

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