scholarly journals Development and verification of frequency domain solution methods for rotor-bearing system responses caused by rolling element bearing waviness

2022 ◽  
Vol 163 ◽  
pp. 108117
Author(s):  
Tuhin Choudhury ◽  
Emil Kurvinen ◽  
Raine Viitala ◽  
Jussi Sopanen
Keyword(s):  
Frequency Domain ◽  
Rolling Element Bearing ◽  
Rolling Element ◽  
Rotor Bearing ◽  
Solution Methods ◽  
Element Bearing ◽  
Bearing System

Dynamic Parameters Estimation and Fault Identification From Random Response of Rolling Element Bearing in a Rotor Bearing System

2019 ◽  
Author(s):  
Pankaj Kumar ◽  
S. Narayanan ◽  
Sayan Gupta
Keyword(s):  
Random Vibration ◽  
Closed Form Solution ◽  
Form Solution ◽  
Parameters Estimation ◽  
Rolling Element Bearing ◽  
Vibration Signals ◽  
Rolling Element ◽  
Rotor Bearing ◽  
Element Bearing ◽  
Bearing System

Abstract This paper presents a procedure for determination of dynamic properties of rolling element bearing by using the vibration signals picked up at the bearing caps. The rotor-bearing assembly is idealized as Duffing oscillator and random vibration signals modelled as exponentially correlated (Ornstein-Uhlenbeck) colored noise. Expressing the excitation as a first order filtered white noise enables the direct formulation of the 3D-Fokker Planck (FP) equation for system response through the Markov vector approach. Closed form solution of the stationary FP equation is derived. Subsequently the response statistics of experimentally obtained random vibration signal are processed through the closed form solution of the FP equation as the inverse process of parameters estimation from the measured response. Further, the dynamic behavior of rigid rotor-bearing system is investigated under combined excitation of white noise and harmonic forces arising due to rotor unbalance force. The effect of system nonlinearities, stiffness, damping and unbalanced excitation force on the dynamic response are investigated using the bifurcation plot. For assessment of structural degradation of bearings, a novel entropy based approach is developed. Experimental studies on roller bearing are carried out to demonstrate the effectiveness of the proposed approach.

Intelligent Fault Diagnosis of Rolling Element Bearing Based on SVMs and Statistical Characteristics

2007 ◽  
Author(s):  
Junyan Yang ◽  
Youyun Zhang ◽  
Yongsheng Zhu ◽  
Qinghua Wang
Keyword(s):  
Frequency Domain ◽  
Wavelet Packet ◽  
Classification Performance ◽  
Rolling Element Bearing ◽  
Statistical Characteristics ◽  
Wavelet Packet Decomposition ◽  
Frequency Bands ◽  
Time Frequency ◽  
Rolling Element ◽  
Element Bearing

In this paper, the statistical characteristics of time, frequency and time-frequency domain are applied to discriminate various fault types and evaluate various fault conditions of rolling element bearing, and the classification performance of them is evaluated by using SVMs. Experimental results showed that the statistical characteristics Mean, Variance, Root, RMS and Peak of the 25 sub frequency bands in frequency domain obtain higher classification accuracy rate on all the fault datasets than the statistical characteristics in the whole time and frequency domain. Wavelet packet decomposition is an efficient time-frequency analysis tool, and it can decompose the original signal into independent frequency bands. Experiment on the statistical characteristics of the 5th level wavelet packet decomposition showed that the statistical characteristics Variance, Root, RMS and Peak can discriminate various fault types and evaluate various fault conditions well on all the datasets. Compared with the statistical characteristics of sub frequency bands in frequency domain, the classification performance of the statistical characteristics of the wavelet packet transform is a little lower than that of the statistical characteristics of sub frequency bands in frequency domain.

Rolling element bearing weak feature extraction based on improved optimal frequency band determination

2018 ◽  
Vol 233 (2) ◽  
pp. 623-634 ◽  
Author(s):  
Rui Yang ◽  
Hongkun Li ◽  
Chaoge Wang ◽  
Changbo He
Keyword(s):  
Frequency Domain ◽  
Frequency Band ◽  
Signal To Noise Ratio ◽  
Wavelet Packet ◽  
Rolling Element Bearing ◽  
Optimal Frequency ◽  
Filter Length ◽  
Iteration Period ◽  
Rolling Element ◽  
Element Bearing

Conventional Kurtosis method represents the statistical property of signal in the time domain. Correlated Kurtosis is proposed that combines the correlation coefficient and Kurtosis in order to indicate the periodicity and impact of signal. In this study, correlated Kurtosis is introduced into frequency domain to improve the recognition accuracy of the optimal frequency band. It does not perform well under the lower signal-to-noise ratio. And then, maximum correlation Kurtosis de-convolution method is used for extracting the approximate impact signal before selecting the optimal frequency band. However, it is limited in diagnosing rolling element bearing fault in the case of the algorithm iteration period is unknown. In addition, filter length also affects the filtering results. To eliminate the confusion, correlated Kurtosis of the frequency domain is applied to iteration period calculation. In this research, a new index is also proposed based on entropy and correlated Kurtosis to optimize the filter length. Then, the full bandwidth of filtered signal is partitioned into several sub-bands according to the refined wavelet packet binary tree. The correlated Kurtosis for each sub-band is calculated. The optimal sub-band for which the correlated Kurtosis is maximal is extracted to analysis. In the end, the efficiency of the new index and the fault diagnosis method are verified by using simulation data and experimental data.

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