A complex filter for vibration signal demodulation in bearing defect diagnosis

2004 ◽  
Vol 276 (1-2) ◽  
pp. 105-119 ◽  
Author(s):  
Yuh-Tay Sheen
Keyword(s):  
Vibration Signal ◽  
Complex Filter ◽  
Bearing Defect ◽  
Signal Demodulation ◽  
Defect Diagnosis

scholarly journals Demodulation of Vibration Signal Based on Envelope-Kurtogram for Ball Bearing Fault Detection

2020 ◽  
Vol 4 (2) ◽  
pp. 115-123
Author(s):  
Berli Paripurna Kamiel
Keyword(s):  
Ball Bearing ◽  
Vibration Signal ◽  
Rolling Element Bearings ◽  
Central Frequency ◽  
Value Analysis ◽  
Bearing Fault ◽  
Bearing Defect ◽  
Rolling Element ◽  
Signal Demodulation ◽  
Envelope Spectrum

Rolling element bearings often suffer damage due to harsh operating and environmental conditions. The method commonly used in detecting faults in a bearing is envelope analysis. However, this method requires setting the central frequency and the correct bandwidth - which corresponds to the resonance frequency of the bearing - for signal demodulation to be effective. This study proposes a kurtogram to determine the correct central frequency and bandwidth to obtain the frequency band with the highest impulse content or the highest kurtosis value. Analysis envelope is applied to the filtered vibration signal using the central frequency and bandwidth parameters obtained from the kurtogram. The results showed that the envelope-kurtogram method is effective for faulty bearing detection as shown in the envelope spectrum where the peaks coincide with the bearing defect characteristic frequency (BPFO) with high accuracy. Likewise, it can be observed several BPFO harmonics which provide information on the level of bearing fault.

Improved Local Mean Decomposition and its Application to Fault Diagnosis of Train Bearing

2013 ◽  
Vol 819 ◽  
pp. 155-159
Author(s):  
Peng Wang ◽  
Huai Xiang Ma
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Processing Method ◽  
Nonstationary Signal ◽  
Practical Application ◽  
Local Mean Decomposition ◽  
Signal Processing Method ◽  
Signal Demodulation ◽  
Improvement Method ◽  
Local Mean

Fault diagnosis of train bearing is an important method to ensure the security of railway. The key to the fault diagnosis is the method of vibration signal demodulation. The local mean decomposition (LMD) is a self-adapted signal processing method which has a good performance in nonlinear nonstationary signal demodulation. The improved LMD method based on kurtosis criterion can prevent errors in the process of calculating the product functions. With the verification of simulation and wheel set experiment, the improvement method has been certified usefully in practical application.

DETERMINATION OF ROLLER BEARING INNER RACE DEFECT BASED ON VIBRATION SIGNAL

2019 ◽  
Vol 24 (3) ◽  
pp. 467-475
Author(s):  
Mohamed El Morsy ◽  
Gabriela Achtenova
Keyword(s):  
Time Domain ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Band Pass Filter ◽  
Bearing Defect ◽  
Rolling Elements ◽  
Characteristic Features ◽  
Bearing Characteristic ◽  
Inner Race

The present article’s intent is to measure and identify the roller bearing inner race defect width and its corresponding characteristic frequency based on filtered time-domain vibration signal. In case localized fault occurs in a bearing, the rolling elements encounter some slippage as the rolling elements enter and leave the bearing load zone. As a consequence, the incidence of the impacts never reproduce exactly at the same position from one cycle to another. Moreover, when the position of the defect is moving with respect to the load distribution zone of the bearing, the series of impulses are modulated in amplitude in time-domain and the conforming Bearing Characteristic Frequencies (BCFs) arise in frequency domain. In order to verify the ability of time-domain in measuring the fault of rolling bearing, an artificial fault is introduced in the vehicle gearbox bearing: an orthogonal placed groove on the inner race with the initial width of 0.6mm approximately. The faulted bearing is a roller bearing quantification of the characteristic features relevant to the inner race bearing defect. It is located on the gearbox input shaft—on the clutch side. To jettison the frequency associated with interferential vibrations, the vibration signal is filtered with a band-pass filter based on an optimal daughter Morlet wavelet function whose parameters are optimized based on maximum Kurtosis (Kurt.). The residual signal is performed for the measurement of defect width. The proposed technique is used to analyse the experimental signal of vehicle gearbox rolling bearing. The experimental test stand is equipped with two dynamometer machines; the input dynamometer serves as an internal combustion engine, the output dynamometer introduces the load on the flange of the output joint shaft. The Kurtosis and Pulse Indicator (PI) are selected as the evaluation parameters of the de-noising effect. The results show the reliability of the proposed approach for identification and quantification of the characteristic features relevant to the inner race bearing defect.

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