A joint resonance frequency estimation and in-band noise reduction method for enhancing the detectability of bearing fault signals

Bozchalooi, Iman Soltani; Liang, Ming

doi:10.1016/j.ymssp.2007.10.006

Cited by 116 publications

(78 citation statements)

References 18 publications

(29 reference statements)

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“…In the context of machine fault detection, a high kurtosis value is treated as a sign of the presence of faults in a rotating mechanical system [8].…”

Section: A Characteristic Of Rolling Element Defect Frequencies and mentioning

confidence: 99%

“…Two major denoising approaches, namely the wavelet threshold-based (also known as decomposition-based) and the wavelet filter-based approach, [3] have been used to filter the vibration signals measured from faulty bearing or gear systems. Bozchalloi and Liang have reported that the reliable condition monitoring would not be possible without proper denoising [8]. The investigation report says the performance of the wavelet based denoising can be improved considerably by preprocessing of the signal using spectral subtraction.…”

Section: Introductionmentioning

confidence: 99%

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Radial Ball Bearing Inner Race Defect Width Measurement using Analytical Wavelet Transform of Acoustic and Vibration Signal

Jena¹,

Singh²,

Kumar³

2012

Measurement Science Review

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In the present work, an experiment is carried out with a customized test setup where the seeded defects are introduced in the form of an axial groove on the inner race of a radial ball bearing. The nature of the acoustic and vibration signal bursts, and their correlation with the inner race defects, are established and estimated. Experimental investigation reveals that the analytical wavelet transform (AWT) is an effective tool for analyzing the acoustic and vibration signals, transmitted from the bearing, in order to characterize and measure the defect size. In the recent work, AWT followed by the time marginal integration (TMI) have been implemented on acoustic and vibration signals of a defective radial bearing. Size of the defect in the inner race of bearing is corroborated well with AWT scalogram. The segregation of the defect is carried out on TMI graph across the highest amplitude spike, which is due to signal burst (due to a contact of ball with bearing inner race defect). This manual demarcation on TMI graph in time axis provides the time duration (contact between a ball and the inner race defect). Using this time duration of the ball passed over bearing inner race defect, RPM of shaft mounted across bearing, and the fundamental train frequency, the defect width is estimated. The deviation of the measured width from the actual, using the proposed method, is sought below 5%. Summarizing, the proposed method can be reckoned a suitable and reliable measurement of radial bearing inner race defect width from acoustic and vibration signal.

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“…In the context of machine fault detection, a high kurtosis value is treated as a sign of the presence of faults in a rotating mechanical system [8].…”

Section: A Characteristic Of Rolling Element Defect Frequencies and mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Radial Ball Bearing Inner Race Defect Width Measurement using Analytical Wavelet Transform of Acoustic and Vibration Signal

Jena¹,

Singh²,

Kumar³

2012

Measurement Science Review

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show abstract

“…However, this would increase cost, complicate hardware and need more operation. The adaptive line enhancement technique, a series of the above background noise cancellation method, is applied to enhance the oil sensor system output signal [4]. The approach applied a delayed scheme in the measured signal which acted as the reference signal obtained through the vibration sensor, and without any additional hardware equipment.…”

Section: Introductionmentioning

confidence: 99%

Oil Debris Signals Enhancement Based On Wavelet Analysis for Wind Turbine Condition Monitoring

Yang

Liang

2016

IJFGCN

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“…稳信号的有力工具，常被用于提取旋转部件振动信号中的故障特征 [4][5] 。文献 [6]基于复平移 Morlet 小波提出了一种自适应的包络解调分析方法，以小波系数的峭度值最大为准则选择合适的基函数匹配故障冲击。针对峭度易受转速影响的缺陷，文献 [7][8] 提出了一种新的指标 --平滑因子 (Smoothness index, SI)用于优化小波参数。此外，信息熵 [9] 、稀疏度 [10] 等指标也常用于设计最优小波进行包络解调。然而，上述指标都是用来描述故障信号的冲击性，无法表征同等重要的循环平稳性特征。为此，文献 [11]提出了一种优化小波参数的新指标--包络谱峰值因子，利用频域的稀疏性来表征时域的循环平稳性 [12] 。更进一步，文献 [13] [10] ( ) ( ) ( )…”

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“…基于上述讨论，我们可以构造一种基于 CE 的多目标优化算法来实现最优 ARLW 滤波。据文献 [13] 中的结论可知，包络的稀疏性可以表征其窄带信号的冲击性特征，包络谱的稀疏性可以表征其窄带信号的循环平稳性特征。对于稀疏性，可以利用峭度 [12,16,19] 、SI [7][8]18] 、稀疏度 [10] 、负熵 [13][14] 等指标度量，本文选取最常用的峭度，并定义第一个目标函数为最大化窄带信号平方包络的峭度： ( ) ( ) …”

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Fault Feature Extraction of Wheel-bearing Based on Multi-objective Cross Entropy Optimization

Gu¹

2018

JME

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Abstract：It is crucial to take impulsiveness and cyclostationarity into account simultaneously in the fault features extraction of wheel-bearing, especially with the occurrence of complex interferences from wheel-rail contact relation. However, these two aspects can hardly be synthesized and balanced by one index. Therefore, a novel multi-objective optimized anti-symmetric real Laplace wavelet filtering method is proposed to deal with this problem. The first fitness function is designed by maximizing the kurtosis value of squared envelope, which is representing the impulsiveness. And, the second fitness function is designed by maximizing the kurtosis value of squared envelope spectrum, which is representing the cyclostationarity. Through combining the non-dominated sorting and crowded comparison, the parameters of the wavelet filter are optimized by the improved cross entropy algorithm, which is immune to impulsive or cyclostationary noises. One vibration signal from a faulty wheel-bearing is investigated to illustrate the effectiveness of the proposed method, and some comparisons with the single-objective method are also conducted to show its superiority in extracting the repetitive transients. Key words： wheel-bearing； fault feature extraction； multi-objective optimization； cross entropy； anti-symmetric real Laplace wavelet 0 前言 *

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A joint resonance frequency estimation and in-band noise reduction method for enhancing the detectability of bearing fault signals

Cited by 116 publications

References 18 publications

Radial Ball Bearing Inner Race Defect Width Measurement using Analytical Wavelet Transform of Acoustic and Vibration Signal

Radial Ball Bearing Inner Race Defect Width Measurement using Analytical Wavelet Transform of Acoustic and Vibration Signal

Oil Debris Signals Enhancement Based On Wavelet Analysis for Wind Turbine Condition Monitoring

Fault Feature Extraction of Wheel-bearing Based on Multi-objective Cross Entropy Optimization

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