Antifriction Bearings Damage Analysis Using Experimental Data Based Models

2013 ◽  
Vol 135 (4) ◽  
Author(s):  
R. G. Desavale ◽  
R. Venkatachalam ◽  
S. P. Chavan
Keyword(s):  
Experimental Data ◽  
Signal Analysis ◽  
Vibration Signal ◽  
Damage Analysis ◽  
Maintenance Strategies ◽  
Outer Race ◽  
Analysis Techniques ◽  
Catastrophic Failures ◽  
Original Contribution ◽  
Inner Race

Diagnosis of antifriction bearings is usually performed by means of vibration signals measured by accelerometers placed in the proximity of the bearing under investigation. The aim is to monitor the integrity of the bearing components, in order to avoid catastrophic failures, or to implement condition based maintenance strategies. In particular, the trend in this field is to combine in a simple theory the different signal-enhancement and signal-analysis techniques. The experimental data based model (EDBM) has been pointed out as a key tool that is able to highlight the effect of possible damage in one of the bearing components within the vibration signal. This paper presents the application of the EDBM technique to signals collected on a test-rig, and be able to test damaged fibrizer roller bearings in different working conditions. The effectiveness of the technique has been tested by comparing the results of one undamaged bearing with three bearings artificially damaged in different locations, namely on the inner race, outer race, and rollers. Since EDBM performances are dependent on the filter length, the most suitable value of this parameter is defined on the basis of both the application and measured signals. This paper represents an original contribution of the paper.

Detecting Damage of Rolling Bearings Using EMD

2005 ◽  
Vol 293-294 ◽  
pp. 753-760 ◽  
Author(s):  
Qiang Gao ◽  
Zheng Jia He ◽  
Xue Feng Chen ◽  
Ke Yu Qi
Keyword(s):  
Mechanical Vibration ◽  
Vibration Signal ◽  
Analysis Method ◽  
Rolling Bearings ◽  
High Frequency Signal ◽  
Outer Race ◽  
Mode Decomposition ◽  
Envelope Signal ◽  
Inner Race ◽  
Emd Method

Empirical mode decomposition (EMD) method is introduced, and a new EMD based approach for damage detection of rolling bearings is presented. In this approach, the characteristic high-frequency signal with amplitude modulation of rolling bearings with local damage is separated from the mechanical vibration signal as an intrinsic mode function (IMF) by using EMD, and an envelope signal can be obtained by using Hilbert transform. Then, the characteristic frequency of damage of rolling bearings is extracted by applying Fourier transform to the envelope signal. The presented approach is used to analyse experimental signals collected from rolling bearings with outer race damage or inner race damage, and the results indicate that the EMD based approach can detect damage of rolling bearings more effectively comparing with traditional envelope analysis method.

Doppler distortion removal based on Dopplerlet transform and re-sampling for wayside fault diagnosis of train bearings

2020 ◽  
pp. 095440622096956
Author(s):  
Ao Zhang ◽  
Changqing Shen ◽  
Qingbo He ◽  
Fei Hu ◽  
Fang Liu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Signal Analysis ◽  
Acoustic Source ◽  
Multiple Sources ◽  
Outer Race ◽  
Sampling Sequence ◽  
Distortion Effect ◽  
Dopplerlet Transform ◽  
Robust To Noise ◽  
Inner Race

In wayside fault diagnosis of train bearings, the phenomenon of Doppler distortion in the acoustic signal of moving acoustic source acquired with a microphone leads to the difficulty for signal analysis. In this paper, a new method based on Dopplerlet transform and re-sampling is proposed to remove the Doppler distortion, and applied in the fault diagnosis of train bearings. Firstly, search the parameters space to find the primary functions-Dopplerlet atoms. According to the Morse acoustic theory and Doppler effect, the instantaneous frequency of the Dopplerlet atom which we choose to remove Doppler distortion of the corresponding acoustic source can be acquired. Then, the re-sampling sequence can be established as the re-sampling vector in time domain. Through the resample, the Doppler distortion effect can be removed. Finally, simulations and experiments with practical acoustic signals of train bearings with a defect on the outer race and the inner race are carried out, and the results verified the effectiveness of this method. Comparing with the other methods of Doppler distortion removal, this method works without measuring the motion parameters in advance, and is quite robust to noise. Meanwhile, this method has the potential to eliminate the Doppler distortion of original signal with multiple sources.

Vibration Characteristics Diagnosis and Estimation of Fault Sizes in Rolling Contact Bearings: A Model Based Approach

2021 ◽  
pp. 1-40
Author(s):  
Imran Jamadar
Keyword(s):  
Experimental Data ◽  
Rolling Contact ◽  
Experimental Methodology ◽  
Vibration Acceleration ◽  
Data Set ◽  
Outer Race ◽  
Model Based ◽  
Computational Performance ◽  
Acceleration Amplitude ◽  
Inner Race

Abstract A novel model based technique is presented in this paper for the estimation of the fault size in different components of rolling contact bearings. A detailed dimensional analysis of the problem is carried out and experimental methodology using Box-Behnken design is applied to generate the experimental data set. First, analysis of the vibration acceleration amplitude at the fault frequency, its dependence on the bearing operating and fault parameters using the obtained vibration data set is carried out by statistical analysis of variance. Numerical equations are developed then using the experimental data set for the correlation of the vibration acceleration amplitude in frequency domain with the fault sizes based on the developed dimensionless terms. A hybrid Back propagation neural network integrating genetic algorithms is also developed so as to check the computational performance of the developed model equations. Validation of the proposed method is carried experimentally also for three seeded defect sizes on outer race, inner race and rolling element. The maximum model accuracy observed is for inner race defect case with predictive accuracy of 99.44 percentage and for the roller defect case it is 98.77 percentage. The deviance observed for the model predictive performance is maximum for outer race defect case with least accuracy of 90.47 percentage amongst all.

scholarly journals Research on transformer condition evaluation technology based on vibration signal analysis

2021 ◽  
Vol 645 ◽  
pp. 012036
Author(s):  
Ma Hao ◽  
Yao Chuang ◽  
Duan Minghui ◽  
Wei Jufang ◽  
Zhang Xin ◽  
...  
Keyword(s):  
Signal Analysis ◽  
Vibration Signal ◽  
Condition Evaluation ◽  
Vibration Signal Analysis

scholarly journals Evolution of Electroencephalogram Signal Analysis Techniques during Anesthesia

Sensors ◽  
2013 ◽  
Vol 13 (5) ◽  
pp. 6605-6635 ◽  
Author(s):  
Mahmoud Al-Kadi ◽  
Mamun Reaz ◽  
Mohd Ali
Keyword(s):  
Signal Analysis ◽  
Analysis Techniques

Research on Roller Bearing with Fault Diagnosis Method Based on EMD and BP Neural Network

2014 ◽  
Vol 1014 ◽  
pp. 501-504 ◽  
Author(s):  
Shu Guo ◽  
You Cai Xu ◽  
Xin Shi Li ◽  
Ran Tao ◽  
Kun Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Classification Performance ◽  
Outer Race ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Main Fault

In order to discover the fault with roller bearing in time, a new fault diagnosis method based on Empirical mode decomposition (EMD) and BP neural network is put forward in the paper. First, we get the fault signal through experiments. Then we use EMD to decompose the vibration signal into a series of single signals. We can extract main fault information from the single signals. The kurtosis coefficient of the single signals forms a feature vector which is used as the input data of the BP neural network. The trained BP neural network can be used for fault identification. Through analyzing, BP neural network can distinguish the fault into normal state, inner race fault, outer race fault. The results show that this method can gain very stable classification performance and good computational efficiency.

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