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.

scholarly journals Multi-objective Informative Frequency Band Selection Based on Negentropy-induced Grey Wolf Optimizer for Fault Diagnosis of Rolling Element Bearings

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1845 ◽  
Author(s):  
Xiaohui Gu ◽  
Shaopu Yang ◽  
Yongqiang Liu ◽  
Rujiang Hao ◽  
Zechao Liu
Keyword(s):  
Frequency Band ◽  
Background Noise ◽  
Grey Wolf Optimizer ◽  
Data Sets ◽  
Rolling Element Bearings ◽  
Grey Wolf ◽  
Objective Functions ◽  
Multi Objective ◽  
Rolling Element ◽  
Envelope Spectrum

Informative frequency band (IFB) selection is a challenging task in envelope analysis for the localized fault detection of rolling element bearings. In previous studies, it was often conducted with a single indicator, such as kurtosis, etc., to guide the automatic selection. However, in some cases, it is difficult for that to fully depict and balance the fault characters from impulsiveness and cyclostationarity of the repetitive transients. To solve this problem, a novel negentropy-induced multi-objective optimized wavelet filter is proposed in this paper. The wavelet parameters are determined by a grey wolf optimizer with two independent objective functions i.e., maximizing the negentropy of squared envelope and squared envelope spectrum to capture impulsiveness and cyclostationarity, respectively. Subsequently, the average negentropy is utilized in identifying the IFB from the obtained Pareto set, which are non-dominated by other solutions to balance the impulsive and cyclostationary features and eliminate the background noise. Two cases of real vibration signals with slight bearing faults are applied in order to evaluate the performance of the proposed methodology, and the results demonstrate its effectiveness over some fast and optimal filtering methods. In addition, its stability in tracking the IFB is also tested by a case of condition monitoring data sets.

New Feature Extraction Method for the Detection of Defects in Rolling Element Bearings

2012 ◽  
Vol 134 (8) ◽  
Author(s):  
Ling Xiang ◽  
Aijun Hu
Keyword(s):  
Ensemble Empirical Mode Decomposition ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
Intrinsic Mode Functions ◽  
Feature Extraction Method ◽  
Outer Race ◽  
Mode Decomposition ◽  
Rolling Element ◽  
Envelope Spectrum ◽  
Detection Of Defects

This paper proposes a new method based on ensemble empirical mode decomposition (EEMD) and kurtosis criterion for the detection of defects in rolling element bearings. Some intrinsic mode functions (IMFs) are presented to obtain symptom wave by EEMD. The different kurtosis of the intrinsic mode function is determined to select the envelope spectrum. The fault feature based on the IMF envelope spectrum whose kurtosis is the maximum is extracted, and fault patterns of roller bearings can be effectively differentiated. Practical examples of diagnosis for a rolling element bearing are provided to verify the effectiveness of the proposed method. The verification results show that the bearing faults that typically occur in rolling element bearings, such as outer-race and inner-race, can be effectively identified by the proposed method.

Spalling Size Evaluation of Rolling Element Bearing Using Acoustic Emission

2013 ◽  
Vol 569-570 ◽  
pp. 497-504 ◽  
Author(s):  
An Bo Ming ◽  
Zhao Ye Qin ◽  
Wei Zhang ◽  
Fu Lei Chu
Keyword(s):  
Acoustic Emission ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
Outer Race ◽  
Cepstrum Analysis ◽  
Rolling Element ◽  
Rolling Elements ◽  
Element Bearing ◽  
Envelope Spectrum ◽  
Size Evaluation

Spalling of the races or rolling elements is one of the most common faults in rolling element bearings. Exact estimation of the spall size is helpful to the life prediction for rolling element bearings. In this paper, the dual-impulsive phenomenon in the response of a spalled rolling element bearing is investigated experimentally, where the acoustic emission signals are utilized. A new method is proposed to estimate the spall size by extracting the envelope of harmonics of the ball passing frequency on the outer race from the squared envelope spectrum. Compared with the cepstrum analysis, the proposed procedure shows more powerful anti-noise ability in the fault size evaluation.

scholarly journals A Novel Method of Frequency Band Selection for Squared Envelope Analysis for Fault Diagnosing of Rolling Element Bearings in a Locomotive Powertrain

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4344 ◽  
Author(s):  
Lang Xu ◽  
Steven Chatterton ◽  
Paolo Pennacchi
Keyword(s):  
Frequency Band ◽  
Modulation Frequency ◽  
New Method ◽  
Statistical Characteristics ◽  
Rolling Element Bearings ◽  
Optimal Frequency ◽  
Envelope Analysis ◽  
Vibration Data ◽  
Rolling Element ◽  
Envelope Spectrum

The development of diagnostics for rolling element bearings (REBs) in recent years makes it possible to detect faults in bearings in real-time. Squared envelope analysis (SEA), in which the statistical characteristics of the squared envelope (SE) or the squared envelope spectrum (SES) are analysed, is widely recognized as both an effective and the simplest method. The most critical step of SEA is to find an optimal frequency band that contains the maximum defect information. The most commonly used approaches for selecting the optimal frequency band are derived from measuring the kurtosis of the SE or the SES. However, most methods fail to cope with the interference of a single or a few impulses in the corresponding domain. A new method is proposed in this paper called “PMFSgram”, which just calculates the kurtosis of the SES in the range centred at the first two harmonics with a span of three times the modulation frequency rather than the entire SES of the band filtered signals. It is possible to avoid most of the interference from a small number of undesired impulses in the SES. PMFSgram uses several bandwidths from 1.5 times to 4.5 times the fault frequency and for each bandwidth has the same number of central frequencies. The frequency setting method is able to select an optimal frequency band containing most of the useful information with less noise. The performance of the new method is verified using synthesized signals and actual vibration data.

Compound faults detection of the rolling element bearing based on the optimal complex Morlet wavelet filter

2017 ◽  
Vol 232 (10) ◽  
pp. 1786-1801 ◽  
Author(s):  
Xiaohui Gu ◽  
Shaopu Yang ◽  
Yongqiang Liu ◽  
Feiyue Deng ◽  
Bin Ren
Keyword(s):  
Particle Filtering ◽  
Morlet Wavelet ◽  
Previous Method ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
Wavelet Filter ◽  
Outer Race ◽  
Rolling Element ◽  
Complex Morlet Wavelet ◽  
Envelope Spectrum

Wavelet filter is widely used in extracting fault features embedded in the noisy vibration signal, especially the complex Morlet wavelet. In most occasions, the filter parameters are optimized adaptively with a suitable objective function. And then, with the Hilbert transform demodulation analysis, the single localized fault in rolling element bearings can be detected. To extend it for compound faults detection, a novel index deduced from the different intervals of the prominent bearing fault frequencies and subsequent harmonics in the envelope spectrum is proposed. By maximizing the ratio of correlated kurtosis to kurtosis of the envelope spectrum amplitudes of the filtered signal, the optimal complex Morlet wavelet filters corresponding to the different faults are designed by the particle filtering method, respectively. Two cases of real signals are analyzed to evaluate the performance of the proposed method, which include one case of experiment signal with artificial outer race fault coupled with roller fault, as well as one case of engineering data with outer race fault coupled with inner race fault. Furthermore, some comparisons with a previous method are also conducted. The results demonstrate the effectiveness and robustness of the method in compound faults diagnosis of the rolling element bearings.

Time-Wavelet Energy Spectral Analysis for Fault Diagnosis of Rolling Element Bearings

2010 ◽  
Vol 34-35 ◽  
pp. 655-660
Author(s):  
Zhi Peng Feng ◽  
Jin Zhang ◽  
Ru Jiang Hao ◽  
Fu Lei Chu ◽  
Xue Jun Li
Keyword(s):  
Spectral Analysis ◽  
Energy Spectrum ◽  
Rolling Element Bearings ◽  
Vibration Signals ◽  
Outer Race ◽  
Wavelet Energy ◽  
Fault Features ◽  
Rolling Element ◽  
Key Indicators ◽  
Inner Race

Periodic impulses in vibration signals and its repeating frequency are the key indicators for diagnosing the localized damage of rolling element bearings. A new method, so called time-wavelet energy spectrum, is proposed to extract the characteristic frequency of faulty elements. It is applied to analyzing the vibration signals of bearings under normal and faulty (with damage on outer race, inner race and ball respectively) statuses. The analysis results show that the time-wavelet energy spectrum is effective in extracting the repeating frequency of periodic impulses. It can not only extract the relatively significant fault feature of outer race damage, but also can extract the weaker fault features of inner race and ball damage.

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.

Experimental investigation on time-domain features in the diagnosis of rolling element bearings by acoustic emission

2021 ◽  
pp. 107754632110161
Author(s):  
Aref Aasi ◽  
Ramtin Tabatabaei ◽  
Erfan Aasi ◽  
Seyed Mohammad Jafari
Keyword(s):  
Acoustic Emission ◽  
Condition Monitoring ◽  
Time Domain ◽  
Central Moment ◽  
Rolling Element Bearings ◽  
Test Rig ◽  
Acoustic Emission Sensor ◽  
Outer Race ◽  
Common Time ◽  
Rolling Element

Inspired by previous achievements, different time-domain features for diagnosis of rolling element bearings are investigated in this study. An experimental test rig is prepared for condition monitoring of angular contact bearing by using an acoustic emission sensor for this purpose. The acoustic emission signals are acquired from defective bearing, and the sensor takes signals from defects on the inner or outer race of the bearing. By studying the literature works, different domains of features are classified, and the most common time-domain features are selected for condition monitoring. The considered features are calculated for obtained signals with different loadings, speeds, and sizes of defects on the inner and outer race of the bearing. Our results indicate that the clearance, sixth central moment, impulse, kurtosis, and crest factors are appropriate features for diagnosis purposes. Moreover, our results show that the clearance factor for small defects and sixth central moment for large defects are promising for defect diagnosis on rolling element bearings.

Simultaneous Monitoring of Rolling-Element and Journal Bearings Using Analysis of Structure-Born Ultrasound Acoustic Emissions

2010 ◽  
Author(s):  
A. Albers ◽  
M. Dickerhof
Keyword(s):  
Acoustic Emission ◽  
Acoustic Emissions ◽  
Piezoelectric Sensor ◽  
Journal Bearings ◽  
Rolling Element Bearings ◽  
Acoustic Emission Sensor ◽  
Outer Race ◽  
Rolling Element ◽  
Characteristic Values ◽  
Acoustic Emission Technology

The application of Acoustic Emission technology for monitoring rolling element or hydrodynamic plain bearings has been addressed by several authors in former times. Most of these investigations took place under idealized conditions, to allow the concentration on one single source of emission, typically recorded by means of a piezoelectric sensor. This can be achieved by either eliminating other sources in advance or taking measures to shield them out (e. g. by placing the acoustic emission sensor very close to the source of interest), so that in consequence only one source of structure-born sound is present in the signal. With a practical orientation this is often not possible. In point of fact, a multitude of potential sources of emission can be worth considering, unfortunately superimposing one another. The investigations reported in this paper are therefore focused on the simultaneous monitoring of both bearing types mentioned above. Only one piezoelectric acoustic emission sensor is utilized, which is placed rather far away from the monitored bearings. By derivation of characteristic values from the sensor signal, different simulated defects can be detected reliably: seeded defects in the inner and outer race of rolling element bearings as well as the occurrence of mixed friction in the sliding surface bearing due to interrupted lubricant inflow.

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