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 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.

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.

scholarly journals Demodulation of Vibration Signal Based on Envelope-Kurtogram for Ball Bearing Fault Detection

2020 ◽  
Vol 4 (2) ◽  
pp. 115-123
Author(s):  
Berli Paripurna Kamiel
Keyword(s):  
Ball Bearing ◽  
Vibration Signal ◽  
Rolling Element Bearings ◽  
Central Frequency ◽  
Value Analysis ◽  
Bearing Fault ◽  
Bearing Defect ◽  
Rolling Element ◽  
Signal Demodulation ◽  
Envelope Spectrum

Rolling element bearings often suffer damage due to harsh operating and environmental conditions. The method commonly used in detecting faults in a bearing is envelope analysis. However, this method requires setting the central frequency and the correct bandwidth - which corresponds to the resonance frequency of the bearing - for signal demodulation to be effective. This study proposes a kurtogram to determine the correct central frequency and bandwidth to obtain the frequency band with the highest impulse content or the highest kurtosis value. Analysis envelope is applied to the filtered vibration signal using the central frequency and bandwidth parameters obtained from the kurtogram. The results showed that the envelope-kurtogram method is effective for faulty bearing detection as shown in the envelope spectrum where the peaks coincide with the bearing defect characteristic frequency (BPFO) with high accuracy. Likewise, it can be observed several BPFO harmonics which provide information on the level of bearing fault.

Frequency band selection based on the kurtosis of the squared envelope spectrum and its application in bearing fault diagnosis

2015 ◽  
Vol 230 (7-8) ◽  
pp. 1113-1125 ◽  
Author(s):  
Chongqing Hu ◽  
Zhongxiao Peng
Keyword(s):  
Frequency Band ◽  
Selection Criterion ◽  
Rolling Element Bearing ◽  
Band Selection ◽  
Planetary Gearbox ◽  
Bearing Fault Diagnosis ◽  
Large Bandwidth ◽  
Rolling Element ◽  
The One ◽  
Envelope Spectrum

Selection of a suitable frequency band for envelope analysis is a key step towards successful diagnosis of rolling element bearing faults. The band selection requirements include: (a) effectively avoiding the frequency range which contains irrelevant periodic impulses, and (b) keeping the feature of impulsiveness for bearing signals. Popular frequency band selection approaches often aim at searching for the band which maximizes the envelope kurtosis or the kurtosis of the envelope spectrum, and thus may not satisfy those requirements simultaneously when weak bearing signals are masked by strong noisy impulses. To resolve this issue, the energy of envelope signal and its energy distribution in the spectrum were taken into consideration in the proposed approach. It was shown that, for weak impulsive signals, the kurtosis of squared envelope spectrum (KSES) decreases as the bandwidth increases provided that the initial band is wide enough to contain most of the signal energy. The decreasing trend of KSES was then used to determine bandwidths for predefined center frequencies. From the obtained narrow bands, the one with a large bandwidth and a low KSES was selected for further analysis by comparing the value of a quality index. The bandwidth determination method and the final band selection criterion effectively excluded irrelevant periodic impulses in the selected band and ensured the high impulsiveness of the extracted signal. The proposed method was validated using experimental data obtained from a bearing rig and a planetary gearbox test rig, and its advantages were highlighted by comparing with fast kurtogram and protrugram.

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.

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 Condition monitoring of induction motor bearing based on bearing damage index

2017 ◽  
Vol 66 (1) ◽  
pp. 105-119 ◽  
Author(s):  
R.K. Patel ◽  
V.K. Giri
Keyword(s):  
Wavelet Packet ◽  
Damage Index ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
Rolling Element ◽  
Non Stationary Signal ◽  
Bearing Damage ◽  
Node Energy

Abstract The rolling element bearings are used broadly in many machinery applications. It is used to support the load and preserve the clearance between stationary and rotating machinery elements. Unfortunately, rolling element bearings are exceedingly prone to premature failures. Vibration signal analysis has been widely used in the faults detection of rotating machinery and can be broadly classified as being a stationary or non-stationary signal. In the case of the faulty rolling element bearing the vibration signal is not strictly phase locked to the rotational speed of the shaft and become “transient” in nature. The purpose of this paper is to briefly discuss the identification of an Inner Raceway Fault (IRF) and an Outer Raceway Fault (ORF) with the different fault severity levels. The conventional statistical analysis was only able to detect the existence of a fault but unable to discriminate between IRF and ORF. In the present work, a detection technique named as bearing damage index (BDI) has been proposed. The proposed BDI technique uses wavelet packet node energy coefficient analysis method. The well-known combination of Hilbert transform (HT) and Fast Fourier Transform (FFT) has been carried out in order to identify the IRF and ORF faults. The results show that wavelet packet node energy coefficients are not only sensitive to detect the faults in bearing but at the same time they are able to detect the severity level of the fault. The proposed bearing damage index method for fault identification may be considered as an ‘index’ representing the health condition of rotating machines.

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