Rolling element bearing instantaneous rotational frequency estimation based on EMD soft-thresholding denoising and instantaneous fault characteristic frequency

2016 ◽  
Vol 23 (7) ◽  
pp. 1682-1689 ◽  
Author(s):  
De-zun Zhao ◽  
Jian-yong Li ◽  
Wei-dong Cheng ◽  
Tian-yang Wang ◽  
Wei-gang Wen
Keyword(s):  
Characteristic Frequency ◽  
Frequency Estimation ◽  
Rotational Frequency ◽  
Rolling Element Bearing ◽  
Soft Thresholding ◽  
Rolling Element ◽  
Element Bearing ◽  
Instantaneous Rotational Frequency ◽  
Fault Characteristic

A fast and adaptive varying-scale morphological analysis method for rolling element bearing fault diagnosis

2012 ◽  
Vol 227 (6) ◽  
pp. 1362-1370 ◽  
Author(s):  
Changqing Shen ◽  
Qingbo He ◽  
Fanrang Kong ◽  
Peter W Tse
Keyword(s):  
Fault Diagnosis ◽  
Morphological Analysis ◽  
Rolling Element Bearing ◽  
Outer Race ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Bearing Defect ◽  
Rolling Element ◽  
Element Bearing ◽  
Fault Characteristic

The research in fault diagnosis for rolling element bearings has been attracting great interest in recent years. This is because bearings are frequently failed and the consequence could cause unexpected breakdown of machines. When a fault is occurring in a bearing, periodic impulses can be revealed in its generated vibration frequency spectrum. Different types of bearing faults will lead to impulses appearing at different periodic intervals. In order to extract the periodic impulses effectively, numerous techniques have been developed to reveal bearing fault characteristic frequencies. In this study, an adaptive varying-scale morphological analysis in time domain is proposed. This analysis can be applied to one-dimensional signal by defining different lengths of the structure elements based on the local peaks of the impulses. The analysis has been first validated by simulated impulses, and then by real bearing vibration signals embedded with faulty impulses caused by an inner race defect and an outer race defect. The results indicate that by using the proposed adaptive varying-scale morphological analysis, the cause of bearing defect could be accurately identified even the faulty impulses were partially covered by noise. Moreover, compared to other existing methods, the analysis can be functioned as an efficient faulty features extractor and performed in a very fast manner.

scholarly journals Feature Extraction of Faulty Rolling Element Bearing under Variable Rotational Speed and Gear Interferences Conditions

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Dezun Zhao ◽  
Jianyong Li ◽  
Weidong Cheng
Keyword(s):  
Feature Extraction ◽  
Rotational Speed ◽  
Vibration Signal ◽  
Rotational Frequency ◽  
Rolling Element Bearing ◽  
Intrinsic Mode Functions ◽  
Auxiliary Equipment ◽  
Time Frequency ◽  
Rolling Element ◽  
Element Bearing

In the field of rolling element bearing fault diagnosis, variable rotational speed and gear noise are main obstacles. Even though some effective algorithms have been proposed to solve the problems, their process is complicated and they may not work well without auxiliary equipment. So we proposed a method of faulty bearing feature extraction based on Instantaneous Dominant Meshing Multiply (IDMM) and Empirical Mode Decomposition (EMD). The new method mainly consists of three parts. Firstly, IDMM is extracted from time-frequency representation of original signal by peak searching algorithm, which can be used to substitute the bearing rotational frequency. Secondly, resampled signal is obtained by an IDMM-based resampling algorithm; then it is decomposed into a number of Intrinsic Mode Functions (IMFs) based on the EMD algorithm. Calculate kurtosis values of IMFs and an appropriate IMF with biggest kurtosis value is selected. Thirdly, the selected IMF is analyzed with envelope demodulation method which can describe the fault type of bearing. The effectiveness of the proposed method has been demonstrated by both simulated and experimental mixed signals which contain bearing and gear vibration signal.

Rolling element bearing weak fault diagnosis based on optimal wavelet scale cyclic frequency extraction

2018 ◽  
Vol 232 (7) ◽  
pp. 895-908 ◽  
Author(s):  
Rui Yang ◽  
Hongkun Li ◽  
Changbo He ◽  
Zhixin Zhang
Keyword(s):  
Wavelet Transform ◽  
Rolling Element Bearing ◽  
Noise Interference ◽  
Characteristic Extraction ◽  
Cyclic Frequency ◽  
Rolling Element ◽  
Transform Coefficients ◽  
Element Bearing ◽  
Weak Fault ◽  
Fault Characteristic

Rolling element bearing fault characteristic information is within the second-order cyclic stationary signal. However, it is susceptible to noise interference. In this article, a new method is proposed for rolling element bearing early fault characteristic extraction according to the cyclic periodogram method. The wavelet transform coefficients are processed and analyzed using the cyclostationary theory. As a result, the implicit cyclic characteristics are contained in wavelet transform coefficients. Therefore, using the modulus or envelope of wavelet transform coefficients instead of the calculation of the cyclic statistics can avoid the window function length selection while maintaining the computation rate. In addition, the calculation of correlated kurtosis is introduced into frequency domain to select optimal wavelet scales. The larger the correlated kurtosis, the stronger the cycle impact characteristic in wavelet coefficients. Calculating the cyclic frequency in the optimal wavelet scale range can accurately extract the weak fault characteristic information. The data processing results demonstrated that the proposed method outperforms existing cyclostationary signal analysis methods in weak fault feature extraction for rolling element bearing.

Contact characteristic and vibration mechanism of rolling element bearing in the process of fault evolution

2021 ◽  
Vol 235 (1) ◽  
pp. 19-36 ◽  
Author(s):  
Wenbing Tu ◽  
Jinwen Yang ◽  
Wennian Yu ◽  
Ya Luo
Keyword(s):  
Fault Diagnosis ◽  
Vibration Response ◽  
Rolling Element Bearing ◽  
Bearing Fault Diagnosis ◽  
Rolling Element ◽  
Vibration Mechanism ◽  
Contact Characteristic ◽  
Element Bearing ◽  
Bearing Vibration ◽  
Fault Evolution

The vibration response of rolling element bearing has a close relation with its fault. An accurate evaluation of the bearing vibration response is essential to the bearing fault diagnosis. At present, most bearing dynamics models are built based on rigid assumptions, which may not faithfully reveal the dynamic characteristics of bearing in the presence of fault. Moreover, previous similar works mainly focus on the fault with a specified size without considering the varying contact characteristics as the fault evolves. This paper developed an explicit dynamics finite element model for the bearing with three types of raceway faults considering the flexibility of each bearing component in order to accurately study the contact characteristic and vibration mechanism of defective bearings in the process of fault evolution. The developed model is validated by comparing its simulation results with both analytical and experimental results. The dynamic contact patterns between the rolling elements and the fault, the additional displacement due to the fault and the faulty characteristics within the bearing vibration signal during the fault evolution process are investigated. The analysis results from this work can provide practitioners an in-depth understanding towards the internal contact characteristics with the existence of raceway fault and theoretical basis for rolling bearing fault diagnosis.

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