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.

scholarly journals Sparse Envelope Spectra for Feature Extraction of Bearing Faults Based on NMF

2019 ◽  
Vol 9 (4) ◽  
pp. 755 ◽  
Author(s):  
Lin Liang ◽  
Lei Shan ◽  
Fei Liu ◽  
Ben Niu ◽  
Guanghua Xu
Keyword(s):  
Feature Extraction ◽  
Frequency Band ◽  
Rolling Bearing ◽  
Weight Vector ◽  
Inner Product ◽  
Bearing Faults ◽  
Feature Extraction Method ◽  
Time Frequency ◽  
Envelope Spectrum ◽  
Transient Oscillations

Periodic impulses and the oscillation response signal are the vital feature indicators of rolling bearing faults. However, finding the suitable feature frequency band is usually difficult due to the interferences of other components and multiple resonance regions. According to the characteristics of non-negative matrix factorization (NMF) on a spectrogram, the feature extraction method from a sparse envelope spectrum for rolling bearing faults is proposed in this paper. On the basis of the time–frequency distribution (TFD) of the periodic transient oscillations, the basic matrix can be interpreted as the spectral bases, and the time weight matrix corresponding to spectral bases can be extracted by NMF. Because the bases and the weights have a one-to-one correspondence, the frequency band filtering with the basic component and the time domain envelope of the weight vector are calculated respectively. Then, the sparse envelope spectrum can be derived by the inner product of the above results. The effectiveness of the proposed method is verified by simulations and experiments. Compared with band-pass filtering and spectral kurtosis methods, and considering the time weights and corresponding the spectral bases for the periodic transient oscillations, the weak fault-rated feature can be enhanced in the sparse spectrum, while other components and noise are weakened. Therefore, the proposed method can reduce the requirement of selecting frequency band filtering.

scholarly journals A Fault Diagnosis Method of Train Wheelset Rolling Bearing Combined with Improved LMD and FK

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Yu Zhang ◽  
Zhuoyou Fan ◽  
Xiaorong Gao ◽  
Lin Luo
Keyword(s):  
Fault Location ◽  
Simulation Analysis ◽  
Spline Interpolation ◽  
Microphone Array ◽  
Computing Time ◽  
Rolling Bearing ◽  
Distortion Correction ◽  
Bearing Defects ◽  
Envelope Spectrum ◽  
The Impact

Trackside acoustic signals contain intense noise and nonstationary features even after Doppler distortion correction. Information on bearing defects in these signals is either weak or heavily attenuated. Thus, an improved compound interpolation envelope local mean decomposition (ICIE LMD) method combined with a fast kurtogram (FK) is proposed for wheelset bearings. In this methodology, cubic Hermite interpolation and cubic spline interpolation are employed to find the envelope of the extremal points in the ICIE LMD algorithm to improve accuracy and decrease the computing time of the decomposed signal component. An FK is sensitive to the impact signal and extracts the fault impact features efficiently. In the application, the proposed method uses ICIE LMD to decompose the multicomponent signal into several specific single product function (PF) components. The kurtosis index of the PF is calculated to select the component which contains the most fault information. Then, the selected component of PF is filtered by FK. Finally, the squared envelope spectrum is used to obtain the fault frequency and identify the fault location. The advantages of the ICIE LMD method are verified by simulation analysis. In the application, the results show that the proposed method efficiently extracts the fault features and enhances the target characteristics of the sound signals from a trackside microphone array. Furthermore, the influence of rotating frequency on locating the fault is reduced.

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.

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.

scholarly journals Weak Fault Feature Extraction of Rolling Bearing Based on SVMD and Improved MOMEDA

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xinyu Wang ◽  
Jie Ma
Keyword(s):  
Fault Location ◽  
Harmonic Component ◽  
Energy Operator ◽  
Rolling Bearing ◽  
Teager Energy Operator ◽  
Mode Decomposition ◽  
Fault Features ◽  
Weak Fault ◽  
Envelope Spectrum ◽  
Teager Energy

In order to solve the problem that it is very difficult to extract fault features directly from the weak impact component of early fault signal of rolling bearing, a method combining continuous variational mode decomposition (SVMD) with modified MOMEDA based on Teager energy operator is proposed. Firstly, the low resonance impulse component in the fault signal is separated from the harmonic component and noise by SVMD, and then the Teager energy operator is used to enhance the impulse feature in the low resonance component to ensure that the accurate fault period is selected by the MOMOEDA algorithm. After further noise reduction by MOMEDA, the envelope spectrum of the signal is analyzed, and finally the fault location is determined. The results of simulation and experimental data show that this method can accurately and effectively extract the characteristic frequency of rolling bearing weak fault.

scholarly journals Improving the Accuracy of Fault Frequency by Means of Local Mean Decomposition and Ratio Correction Method for Rolling Bearing Failure

2019 ◽  
Vol 9 (9) ◽  
pp. 1888 ◽  
Author(s):  
Yongqiang Duan ◽  
Chengdong Wang ◽  
Yong Chen ◽  
Peisen Liu
Keyword(s):  
Fault Location ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Correction Method ◽  
Bearing Failure ◽  
Local Mean Decomposition ◽  
Local Mean ◽  
Hilbert Envelope ◽  
Ratio Correction ◽  
Envelope Spectrum

The fault frequencies are as they are and cannot be improved. One can only improve its estimation quality. This paper proposes a fault diagnosis method by combining local mean decomposition (LMD) and the ratio correction method to process the short-time signals. Firstly, the vibration signal of rolling bearing is decomposed into a series of product functions (PFs) by LMD. The PF, which contains the richest fault information, is selected to perform envelope spectrum analysis by the Hilbert transform (HT). Secondly, the Hilbert envelope spectrum of the selected PF is corrected with the ratio correction method. Finally, higher precision fault frequencies are extracted from the corrected Hilbert envelope spectrum, and then the fault location is accurately determined. The proposed method of this paper can be used in online real-time monitoring technology of rolling bearing failure.

scholarly journals A Novel Method for Extracting Maximum Kurtosis Component and Its Applications in Rolling Bearing Fault Diagnosis

2019 ◽  
Vol 2019 ◽  
pp. 1-17
Author(s):  
Yonggang Xu ◽  
Zeyu Fan ◽  
Kun Zhang ◽  
Chaoyong Ma
Keyword(s):  
Fault Diagnosis ◽  
Frequency Band ◽  
Rolling Bearing ◽  
Rolling Bearings ◽  
Frequency Bands ◽  
Bearing Fault Diagnosis ◽  
Important Index ◽  
Novel Method ◽  
Simulation Signal ◽  
Fault Information

Rolling bearing plays an important role in the overall operation of the mechanical system; therefore, it is necessary to monitor and diagnose the bearings. Kurtosis is an important index to measure impulses. Fast Kurtogram method can be applied to the fault diagnosis of rolling bearings by extracting maximum kurtosis component. However, the final result may disperse the effective fault information to different frequency bands or find wrong frequency band, resulting in inaccurate frequency band selection or misdiagnosis. In order to find the maximum component of kurtosis accurately, an algorithm of frequency band multidivisional and overlapped based on EWT (MDO-EWT) is proposed in this paper. This algorithm changes the traditional Fast Kurtogram frequency bands division method and filtering method. It builds the EWT boundaries based on the maximum kurtosis component in each iteration and finally obtains the maximum kurtosis component. Through the simulation signal and the rolling bearing inner and outer ring fault signals verification, it is proved that the proposed method has a good performance on accuracy and effectiveness.

scholarly journals Spillovers of Stock Markets among the BRICS: New Evidence in Time and Frequency Domains before the Outbreak of COVID-19 Pandemic

2021 ◽  
Vol 14 (3) ◽  
pp. 112
Author(s):  
Kai Shi
Keyword(s):  
Stock Market ◽  
Frequency Band ◽  
Stock Markets ◽  
Emerging Market ◽  
Low Frequency ◽  
Systematic Risk ◽  
Error Variance ◽  
Frequency Bands ◽  
Volatility Spillovers ◽  
Risk Sources

We attempted to comprehensively decode the connectedness among the abbreviation of five emerging market countries (BRICS) stock markets between 1 August 2002 and 31 December 2019 not only in time domain but also in frequency domain. A continuously varying spillover index based on forecasting error variance decomposition within a generalized abbreviation of vector-autoregression (VAR) framework was computed. With the help of spectral representation, heterogeneous frequency responses to shocks were separated into frequency-specific spillovers in five different frequency bands to reveal differentiated linkages among BRICS markets. Rolling sample analyses were introduced to allow for multiple changes during the sample period. It is found that return spillovers dominated by the high frequency band (within 1 week) part declined with the drop of frequencies, while volatility spillovers dominated by the low frequency band (above 1 quarter) part grew with the decline in frequencies; the dynamics of spillovers were influenced by crucial systematic risk events, and some similarities implied in the spillover dynamics in different frequency bands were found. From the perspective of identifying systematic risk sources, China’s stock market and Russia’s stock market, respectively, played an influential role for return spillover and volatility spillover across BRICS markets.

Quantitative acoustic emission investigation on the crack evolution in concrete prisms by frequency analysis based on wavelet packet transform

2021 ◽  
pp. 147592172110188
Author(s):  
Zonglian Wang ◽  
Keqin Ding ◽  
Huilan Ren ◽  
Jianguo Ning
Keyword(s):  
Acoustic Emission ◽  
Frequency Band ◽  
Wavelet Packet ◽  
Strain Energy Release ◽  
Crack Size ◽  
Frequency Bands ◽  
Emission Signal ◽  
Damage Process ◽  
Concrete Materials ◽  
Stage 1

To gain an insight into the evolution of micro-cracks in concrete materials, a quantitative acoustic emission investigation on the damage process of concrete prisms subjected to three-point bending loading was performed. Each of the monitored acoustic emission signals was processed by a two-level wavelet packet decomposition into four different frequency bands (AA2, DA2, AD2, and DD2), and the energy coefficients R1, R2, R3, and R4 that parameterize their characteristic frequency bands were calculated. By analyzing variations in energy coefficients of the lowest frequency band (AA2), R1, and the energy coefficients of the highest frequency band (DD2), R4, the whole damage process was divided into three stages: crack initiation, crack growth, and crack coalescence. An inverse relationship between the frequency of the acoustic emission signal emitted by the propagating crack and the crack size in concrete materials was acquired based on the damage theory of brittle materials and the strain energy release theory. The statistical analysis results of the experimental data indicated that the average of R1 increased in turn, and the average of R4 correspondingly decreased in turn from Stage 1 to Stage 3. It revealed that the frequencies of acoustic emission signals decreased gradually with the evolution of the damage of concrete prisms, which is in a good agreement with the theoretical analysis result.

An Effective Brain Imaging Biomarker for AD and aMCI: ALFF in Slow-5 Frequency Band

2021 ◽  
Vol 18 ◽  
Author(s):  
Luoyu Wang ◽  
Qi Feng ◽  
Mei Wang ◽  
Tingting Zhu ◽  
Enyan Yu ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Frequency Band ◽  
Low Frequency ◽  
Posterior Cingulate Cortex ◽  
Angular Gyrus ◽  
Imaging Biomarker ◽  
Support Vector ◽  
Frequency Bands ◽  
Local Functional ◽  
Left Angular Gyrus

Background: As a potential brain imaging biomarker, amplitude of low frequency fluc-tuation (ALFF) has been used as a feature to distinguish patients with Alzheimer’s disease (AD) and amnestic mild cognitive impairment (aMCI) from normal controls (NC). However, it remains unclear whether the frequency-dependent pattern of ALFF alterations can effectively distinguish the different phases of the disease. Methods: In the present study, 52 AD and 50 aMCI patients were enrolled together with 43 NC in total. The ALFF values were calculated in the following three frequency bands: classical (0.01-0.08 Hz), slow-4 (0.027-0.073 Hz) and slow-5 (0.01-0.027 Hz) for the three different groups. Subsequently, the local functional abnormalities were employed as features to examine the effect of classification among AD, aMCI and NC using a support vector machine (SVM). Results: We found that the among-group differences of ALFF in the different frequency bands were mainly located in the left hippocampus (HP), right HP, bilateral posterior cingulate cortex (PCC) and bilateral precuneus (PCu), left angular gyrus (AG) and left medial prefrontal cortex (mPFC). When the local functional abnormalities were employed as features, we identified that the ALFF in the slow-5 frequency band showed the highest accuracy to distinguish among the three groups. Conclusion: These findings may deepen our understanding of the pathogenesis of AD and suggest that slow-5 frequency band may be helpful to explore the pathogenesis and distinguish the phases of this disease.

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