AN ANALYTICAL MODEL FOR THE PREDICTION OF THE VIBRATION RESPONSE OF ROLLING ELEMENT BEARINGS DUE TO A LOCALIZED DEFECT

1997 ◽  
Vol 205 (3) ◽  
pp. 275-292 ◽  
Author(s):  
N. Tandon ◽  
A. Choudhury
Keyword(s):  
Analytical Model ◽  
Vibration Response ◽  
Rolling Element Bearings ◽  
Rolling Element

A PEAK ENERGY CRITERION (P. E.) FOR THE SELECTION OF RESONANCE BANDS IN COMPLEX SHIFTED MORLET WAVELET (CSMW) BASED DEMODULATION OF DEFECTIVE ROLLING ELEMENT BEARINGS VIBRATION RESPONSE

2009 ◽  
Vol 07 (04) ◽  
pp. 387-410 ◽  
Author(s):  
KONSTANTINOS C. GRYLLIAS ◽  
IOANNIS ANTONIADIS
Keyword(s):  
Energy Criterion ◽  
Vibration Response ◽  
Frequency Resolution ◽  
Center Frequency ◽  
Index Number ◽  
Rolling Element Bearings ◽  
Time Frequency ◽  
Peak Energy ◽  
Rolling Element ◽  
Selection Of

Complex Shifted Morlet Wavelets (CSMW) present a number of advantages when used for the demodulation of the vibration response of defective rolling element bearings: (A) They present the optimally located window simultaneously in the time and in the frequency domains; (B) They allow for the maximal time-frequency resolution; (C) The magnitudes of the complex wavelet coefficients in the time domain lead directly to the required envelope; (D) They allow for the optimal selection of both the center frequency and the bandwidth of the requested filter. A Peak Energy criterion (P. E.) is proposed in this paper for the simultaneous automatic selection of both the center frequency and the bandwidth of the relevant wavelet window to be used. As shown in a number of application cases, this criterion presents a more effective behavior than other criteria used (Crest Factor, Kurtosis, Smoothness Index, Number of Peaks), since it combines the advantages of energy based criteria, with criteria characterizing the spikiness of the response.

Sensitivity Analysis of an Information Maximization Approach to the Blind Separation of the Vibration Responses of Defective Rolling Element Bearings

2005 ◽  
Author(s):  
C. Yiakopoulos ◽  
I. Antoniadis
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Linear Function ◽  
Network Structure ◽  
Vibration Response ◽  
Rolling Element Bearings ◽  
Rolling Element ◽  
Non Linear ◽  
Vibration Responses ◽  
Neural Network Structure

Vibration response of rotating machines is typically mixed and corrupted by a variety of interfering sources and noise, leading to the necessity for the isolation of the useful signal components. A relevant frequently encountered industrial case is the need for the separation of the vibration responses of the same type of bearings inside the same machine. For this purpose, a Blind Source Separation procedure has been successfully applied, based on the maximization of the information transferred in a neural network structure. Thus, a key element for the success of the proposed procedure is the non-linear function used in this single layer Neural Network structure. However, since the vibration response of defective rolling element bearings is characterized by signals with super-Gaussian distributions, a sensitivity analysis of this non-linear function is necessary. First, this analysis is performed in a set of numerical experiments, based on dynamic models of defective bearings. Finally, the same analysis is applied in an experimental test rig.

A Theoretical Model to Predict Vibration Response of Rolling Bearings to Distributed Defects Under Radial Load

1998 ◽  
Vol 120 (1) ◽  
pp. 214-220 ◽  
Author(s):  
A. Choudhury ◽  
N. Tandon
Keyword(s):  
Theoretical Model ◽  
Discrete Spectrum ◽  
Analytical Model ◽  
Relative Frequency ◽  
Vibration Response ◽  
Radial Load ◽  
Rolling Bearings ◽  
Rolling Element ◽  
Amplitude Level ◽  
Inner Race

An analytical model has been presented to predict the vibration response of rolling bearings due to distributed defects under radial load. For bearings without defect and with race defect, the model predicts a discrete spectrum with components at outer and inner race characteristic defect frequencies for the response of the respective races. The amplitude level for race defect significantly increases at the respective frequencies in comparison to the response of a bearing without defect. For a bearing with off-size rolling element, the response is at the relative frequency of cage with respect to the frequency of motion of the corresponding race.

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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