The Shock Pulse Index and Its Application in the Fault Diagnosis of Rolling Element Bearings

Sun, Peng; Liao, Yuhe; Lin, Jin

doi:10.3390/s17030535

Cited by 17 publications

(5 citation statements)

References 29 publications

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“…SNR can enhance signal quality and minimize the impact of noise on fault signals, and PRD can assess the divergence between the original and reconstructed signals. Drawing from the construction principles outlined in Sun et al [29], and to further enhance the index's trend of change, we introduce the combined weight index SP, as shown in Equation (10).…”

Section: Sp Portfoliomentioning

confidence: 99%

Fault Feature Extraction Method of Rolling Bearing Based on IAFD and TKEO

Guo,

Ma,

Xiong

et al. 2024

Journal of Sensors

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The study of bearing fault feature extraction using adaptive Fourier decomposition (AFD) holds significant practical importance. However, AFD is constrained by its reliance on prior knowledge for determining decomposition levels, which can result in either underdecomposition or overdecomposition based on a single indicator. Consequently, an improved adaptive Fourier decomposition (IAFD) is proposed. First, a combined weight index called SP is constructed, and the whale optimization algorithm is employed to optimize the SP weight parameter. Second, the IAFD decomposition levels can be adaptively determined using the optimized SP. Finally, a feature extraction method-based IAFD and Teager–Kaiser energy operator is applied in rolling bearing fault diagnosis. Case studies on the Case Western Reserve University and self-made KUST-SY datasets validate the effectiveness of the proposed method.

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Section: Sp Portfoliomentioning

confidence: 99%

Fault Feature Extraction Method of Rolling Bearing Based on IAFD and TKEO

Guo,

Ma,

Xiong

et al. 2024

Journal of Sensors

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“…A higher value of the kurtosis index represents the periodic impulsive features of the signal are richer. But kurtosis is very sensitive to the accidental impulses [ 31 ]. When an accidental impulse appears, the kurtosis value will increase sharply, which may lead to erroneous evaluations.…”

Section: Theory Backgroundmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Based on Optimal Notch Filter and Enhanced Singular Value Decomposition

Pang

Tang

et al. 2018

Entropy

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The impulsive fault feature signal of rolling bearings at the early failure stage is easily contaminated by the fundamental frequency (i.e., the rotation frequency of the shaft) signal and background noise. To address this problem, this paper puts forward a rolling bearing weak fault diagnosis method with the combination of optimal notch filter and enhanced singular value decomposition. Firstly, in order to eliminate the interference of the fundamental frequency signal, the original signal was processed by the notch filter with the fundamental frequency as the center frequency and with a varying bandwidth to get a series of corresponding notch filter signals. Secondly, the Teager energy entropy index was adopted to adaptively determine the optimal bandwidth to complete the optimal notch filter analysis on the raw vibration signal and obtain the corresponding optimal notch filter signal. Thirdly, an enhanced singular value decomposition de-nosing method was employed to de-noise the optimal notch filter signal. Finally, the envelope spectrum analysis was conducted on the de-noised signal to extract the fault characteristic frequencies. The effectiveness of the presented method was demonstrated via simulation and experiment verifications. In addition, the minimum entropy deconvolution, Kurtogram and Infogram methods were employed for comparisons to show the advantages of the presented method.

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“…To compute the autocorrelation function of stationary signal s with the length of N, firstly, obtain the envelope signal by Hilbert transform of the measured signal [23,32]:…”

Section: Autocorrelation Function Impulse Harmonic To Noise Ratio Indexmentioning

confidence: 99%

Incipient Fault Feature Extraction of Rolling Bearings Using Autocorrelation Function Impulse Harmonic to Noise Ratio Index Based SVD and Teager Energy Operator

Zheng

Zhang

et al. 2017

Applied Sciences

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Abstract:The periodic impulse feature is the most typical fault signature of the vibration signal from fault rolling element bearings (REBs). However, it is easily contaminated by noise and interference harmonics. In order to extract the incipient impulse feature from the fault vibration signal, this paper presented an autocorrelation function periodic impulse harmonic to noise ratio (ACFHNR) index based on the SVD-Teager energy operator (TEO) method. Firstly, the Hankel matrix is constructed based on the raw vibration fault signal of rolling bearing, and the SVD method is used to obtain the singular components. Afterwards, the ACFHNR index is employed to measure the abundance of the periodic impulse fault feature for the singular components, and the component with the largest ACFHNR index value is extracted. Moreover, the properties of the ACFHNR index are demonstrated by simulations and the full life cycle of the experiment, showing its superiority over the traditional kurtosis and root mean square (RMS) index for extracting and detecting incipient periodic impulse features. Finally, the Teager energy operator spectrum of the extracted informative signal is gained. The simulation and experimental results indicated that the proposed ACFHNR index based method can effectively detect the incipient fault feature of the rolling bearing, and it shows better performance than the kurtosis and RMS index based methods.Keywords: rolling element bearings (REBs); singular value decomposition (SVD); autocorrelation function impulse harmonic to noise ratio (ACFHNR); teager energy operator (TEO)

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The Shock Pulse Index and Its Application in the Fault Diagnosis of Rolling Element Bearings

Cited by 17 publications

References 29 publications

Fault Feature Extraction Method of Rolling Bearing Based on IAFD and TKEO

Fault Feature Extraction Method of Rolling Bearing Based on IAFD and TKEO

Rolling Bearing Fault Diagnosis Based on Optimal Notch Filter and Enhanced Singular Value Decomposition

Incipient Fault Feature Extraction of Rolling Bearings Using Autocorrelation Function Impulse Harmonic to Noise Ratio Index Based SVD and Teager Energy Operator

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