scholarly journals Novel Technology Based on the Spectral Kurtosis and Wavelet Transform for Rolling Bearing Diagnosis

2020 ◽  
Vol 4 (2) ◽  
Author(s):  
Len Gelman ◽  
Tejas H. Patel ◽  
Gabrijel Persin ◽  
Brian Murray ◽  
Allan Thomson
Keyword(s):  
Wavelet Transform ◽  
Frequency Band ◽  
Rolling Bearing ◽  
Band Selection ◽  
Spectral Kurtosis ◽  
Novel Technology ◽  
Rolling Element ◽  
Feature Calculation ◽  
Inner Race ◽  
Selection Of

A novel diagnosis technology combining the benefits of spectral kurtosis and wavelet transform is proposed and validated for early defect diagnosis of rolling element bearings. A systematic procedure for feature calculation is proposed and rules for selection of technology parameters are explained. Experimental validation of the proposed method carried out for early detection of the inner race defect. A comparison between frequency band selection through wavelets and spectral kurtosis is also presented. It has been observed that the frequency band selected using spectral kurtosis provide better separation between healthy and defective bearings compared to the frequency band selection using wavelet. In terms of Fisher criterion the use of spectral kurtosis has a gain of 2.75 times compared to the wavelet.

scholarly journals Rolling Bearing Fault Feature Extraction Based on Adaptive Tunable Q-Factor Wavelet Transform and Spectral Kurtosis

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Jianlong Zhao ◽  
Yongchao Zhang ◽  
Qingguang Chen
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Frequency Band ◽  
Rolling Bearing ◽  
Band Pass Filter ◽  
Q Factor ◽  
Bearing Fault ◽  
Spectral Kurtosis ◽  
Manual Selection ◽  
Weak Fault

The fault feature of the rolling bearing is difficult to extract when weak fault occurs and interference exists. The tunable Q-factor wavelet transform (TQWT) can effectively extract the weak fault characteristic of the rolling bearing, but the manual selection of the Q-factor affects the decomposition result and only using TQWT presents interference. Aiming at the above problems, an adaptive tunable Q-factor wavelet transform (ATQWT) and spectral kurtosis (SK) method is proposed in this paper. Firstly, the method applies particle swarm optimization (PSO) to seek the optimized Q-factor for avoiding manual selection, which uses the kurtosis value of the transient impact component as the particle fitness function. The rolling bearing fault signal is decomposed into continuous oscillation component and transient impact component containing fault feature by the optimized Q-factor. Then, due to the presence of interference in the decomposition result of ATQWT, the SK analysis of the transient impact component is used to determine the frequency band of periodic impact component characterizing fault feature by fast kurtogram. Finally, the band-pass filter established through the above frequency band is employed to filter the interference in the transient impact component. Simulation and experimental results indicate that the ATQWT can highlight the periodic impact component characterizing rolling bearing fault feature, and the SK can filter interference in the transient impact component, which improves feature extraction effect and has great significance to enhance fault diagnosis accuracy of the rolling bearing. Compared with EEMD-TQWT and TQWT-SK, the fault feature extracted by the proposed method is prominent and effective.

Optimal Frequency Band Selection for the Square Envelope Spectrum in the Diagnostics of Rolling Element Bearings

2014 ◽  
Author(s):  
S. Chatterton ◽  
P. Borghesani ◽  
P. Pennacchi ◽  
A. Vania
Keyword(s):  
Frequency Band ◽  
Vibration Signal ◽  
Band Selection ◽  
Rolling Element Bearings ◽  
Optimal Frequency ◽  
Simple Method ◽  
Rolling Element ◽  
Envelope Spectrum ◽  
Bearing Damage ◽  
Selection Of

Diagnostics of rolling element bearings is usually performed by means of a second-order cyclostationary tool applied to the vibration signal, due to the stochastic nature of the contact between the defect and the bearing rolling elements. The most used and simple method is the Envelope Analysis that is based on the identification of bearing damage frequency components in the so-called Square Envelope Spectrum. The main critical point of this technique is the selection of a suitable frequency band for the demodulation of the vibration signal. The most used approach for the frequency band selection is based on the evaluation of the band-Kurtosis index by mean of diagrams as the frequently used Fast Kurtogram or the more recent Protrugram. Both of them may fail in the selection of the optimal frequency band when other vibration sources affect the Kurtosis index. Also critical is the constancy in the time of this optimal band. In the paper, an experimental case of a bearing damage is investigated and an alternative approach for the filter band selection, the so-called “PeaksMap”, will be proposed by the authors and compared with the ones available in the literature.

Defect Detection for Bearings Using Envelope Spectra of Wavelet Transform

2004 ◽  
Vol 126 (4) ◽  
pp. 567-573 ◽  
Author(s):  
D. F. Shi ◽  
W. J. Wang ◽  
L. S. Qu
Keyword(s):  
Wavelet Transform ◽  
Resonant Frequency ◽  
Frequency Band ◽  
Defect Detection ◽  
Rolling Element Bearings ◽  
New Approach ◽  
Outer Race ◽  
Rolling Element ◽  
Envelope Spectrum ◽  
Inner Race

In order to overcome the shortcomings in the traditional envelope analysis in which manually specifying a resonant frequency band is required, a new approach based on the fusion of the wavelet transform and envelope spectrum is proposed for detecting and localizing defects in rolling element bearings. This approach is capable of completely extracting the characteristic frequencies related to the defect from the resonant frequency band. Based on the Shannon entropy of wavelet-based envelope spectra, a criterion to select optimal scale to monitor the condition of bearings is also presented. Experiment results show that the proposed approach is sensitive and reliable in detecting defects on the outer race, inner race, and rollers of bearings.

Median line-gram and its application in the fault diagnosis of rolling bearing

2022 ◽  
Author(s):  
Xinglong Wang ◽  
Jinde Zheng ◽  
Jun Zhang
Keyword(s):  
Frequency Band ◽  
Fault Location ◽  
Rolling Bearing ◽  
Suggested Method ◽  
Median Line ◽  
Frequency Bands ◽  
Unique Approach ◽  
Envelope Spectrum ◽  
Line Segmentation ◽  
Selection Of

Abstract The level selection of frequency band division structure relies on previous information in most gram approaches that capture the optimal demodulation frequency band (ODFB). When an improper level is specified in these approaches, the fault characteristic information contained in the produced ODFB may be insufficient. This research proposes a unique approach termed median line-gram (MELgram) to tackle the level selection problem. To divide the frequency domain signal into a series of demodulation frequency bands, a spectrum median line segmentation model based on Akima interpolation is first created. The level and boundary of the segmentation model can be adaptively identified by this means. Second, the acquired frequency bands are quantized using the negative entropy index, and the ODFB is defined as the frequency band with the largest value. Third, the envelope spectrum is used to determine the ODFB characteristic frequency to pinpoint the bearing fault location. Finally, both simulation and experimental signal analysis are used to demonstrate the efficiency of the suggested method. Furthermore, the suggested method extracts more defect feature information from the ODFB than existing methods.

A Modified EEMD Decomposition for the Detection of Rolling Bearing Faults

2014 ◽  
Vol 548-549 ◽  
pp. 369-373
Author(s):  
Yuan Cheng Shi ◽  
Yong Ying Jiang ◽  
Hai Feng Gao ◽  
Jia Wei Xiang
Keyword(s):  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Hilbert Spectrum ◽  
Bearing Faults ◽  
Mode Decomposition ◽  
Fault Features ◽  
Rolling Element ◽  
Inner Race

The vibration signals of rolling element bearings are non-linear and non-stationary and the corresponding fault features are difficult to be extracted. EEMD (Ensemble empirical mode decomposition) is effective to detect bearing faults. In the present investigation, MEEMD (Modified EEMD) is presented to diagnose the outer and inner race faults of bearings. The original vibration signals are analyzed using IMFs (intrinsic mode functions) extracted by MEEMD decomposition and Hilbert spectrum in the proposed method. The numerical and experimental results of the comparison between MEEMD and EEMD indicate that the proposed method is more effective to extract the fault features of outer and inner race of bearings than EEMD.

Research on the Order Selection of the Autoregressive Modelling for Rolling Bearing Diagnosis

2010 ◽  
Vol 224 (10) ◽  
pp. 2289-2297 ◽  
Author(s):  
F Cong ◽  
J Chen ◽  
G Dong
Keyword(s):  
Rolling Bearing ◽  
Rolling Element Bearing ◽  
Ar Model ◽  
Optimal Order ◽  
Periodic Part ◽  
Order Selection ◽  
Rolling Element ◽  
Resonance Damping ◽  
The One ◽  
Selection Of

The article does a research on the order selection of the autoregressive (AR) model for the rolling element bearings. First, the model of the signal that matches the one introduced by McFadden is considered. To clearly describe it, here it is called the resonance damping model. It is shown that the impulses generated by a fault will cause structure resonance and soon decay with a periodic mode. As the AR process based on the prediction theory has an ability to recognize the periodic (quasi-periodic) part, it is possible to pick out the damping part that is a quasi-periodic component. Because of the background noise, the damping part cannot be recognized if the component decays to be buried into noise absolutely. Hence the optimal order should be the number of points contained by the process, which is the maximum length of the periodic damping part that the AR model can recognize. That is to say, the process should last until the resonance damping part is buried into noise completely. Then an experiment to validate the method is carried out and success is achieved in the fault diagnosis of real rolling bearings. In the end, it is concluded that the optimal order has a high ability for noise cancellation for rolling element bearing diagnosis.

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