Roller bearing acoustic signature extraction by wavelet packet transform, applications in fault detection and size estimation

Hemmati, Farzad; Orfali, Wasim; Gadala, Mohamed S.

doi:10.1016/j.apacoust.2015.11.003

Cited by 137 publications

(71 citation statements)

References 26 publications

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“…This can be calculated using Equation (3) [24,25], where, is the ratio of the total energy of the sub-length acoustic signal to the total energy of the entire acoustic signal. Figure 2.…”

Section: Acoustic Signal Monitoringmentioning

confidence: 99%

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A Comparison Study of Kernel Functions in the Support Vector Machine and Its Application for Termite Detection

et al. 2018

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Abstract:Termites are the most destructive pests and their attacks significantly impact the quality of wooden buildings. Due to their cryptic behavior, it is rarely apparent from visual observation that a termite infestation is active and that wood damage is occurring. Based on the phenomenon of acoustic signals generated by termites when attacking wood, we proposed a practical framework to detect termites nondestructively, i.e., by using the acoustic signals extraction. This method has the pros to maintain the quality of wood products and prevent higher termite attacks. In this work, we inserted 220 subterranean termites into a pine wood for feeding activity and monitored its acoustic signal. The two acoustic features (i.e., energy and entropy) derived from the time domain were used for this study's analysis. Furthermore, the support vector machine (SVM) algorithm with different kernel functions (i.e., linear, radial basis function, sigmoid and polynomial) were employed to recognize the termites' acoustic signal. In addition, the area under a receiver operating characteristic curve (AUC) was also adopted to analyze and improve the performance results. Based on the numerical analysis, the SVM with polynomial kernel function achieves the best classification accuracy of 0.9188.

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“…This can be calculated using Equation (3) [24,25], where, is the ratio of the total energy of the sub-length acoustic signal to the total energy of the entire acoustic signal. Figure 2.…”

Section: Acoustic Signal Monitoringmentioning

confidence: 99%

“…Whereas, entropy (H) is depicted as a measure of abrupt changes in the energy level from the acoustic signal. This can be calculated using Equation (3) [24,25], where, e j is the ratio of the total energy of the sub-length acoustic signal to the total energy of the entire acoustic signal.…”

Section: Acoustic Signal Monitoringmentioning

confidence: 99%

A Comparison Study of Kernel Functions in the Support Vector Machine and Its Application for Termite Detection

et al. 2018

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“…On the basis of infogram, the multiscale clustering grey infogram (MCGI) was proposed by Li et al [14]. Hemmati et al proposed an index that combines kurtosis and Shannon entropy, and the index was used to obtain the optimal band pass filter utilizing wavelet packet transform (WPT) and envelope detection [15]. The MCGI combined the negentropies of the time and frequency domains in a grey fashion using multiscale clustering.…”

Section: Introductionmentioning

confidence: 99%

Teager Energy Entropy Ratio of Wavelet Packet Transform and Its Application in Bearing Fault Diagnosis

Wan

Zhang

2018

Entropy

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Abstract:Kurtogram can adaptively select the resonant frequency band, and then the characteristic fault frequency can be obtained by analyzing the selected band. However, the kurtogram is easily affected by random impulses and noise. In recent years, improvements to kurtogram have been concentrated on two aspects: (a) the decomposition method of the frequency band; and (b) the selection index of the optimal frequency band. In this article, a new method called Teager Energy Entropy Ratio Gram (TEERgram) is proposed. The TEER algorithm takes the wavelet packet transform (WPT) as the signal frequency band decomposition method, which can adaptively segment the frequency band and control the noise. At the same time, Teager Energy Entropy Ratio (TEER) is proposed as a computing index for wavelet packet subbands. WPT has better decomposition properties than traditional finite impulse response (FIR) filtering and Fourier decomposition in the kurtogram algorithm. At the same time, TEER has better performance than the envelope spectrum or even the square envelope spectrum. Therefore, the TEERgram method can accurately identify the resonant frequency band under strong background noise. The effectiveness of the proposed method is verified by simulation and experimental analysis.

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“…In the past years, many vibration signal processing techniques, such as wavelet packet transform [5], ensemble empirical mode decomposition (EEMD) [6], local mean decomposition (LMD) algorithm [7] and Variational mode decomposition (VMD) algorithm [8], higher order energy operator fusion [9], Quaternion singular spectrum [10], blind source separation method [11], low-rank matrix approximations [12], sparse representation [13,14] and deep learning [15], etc., have been developed to extract the fault features from measured signals for bearing fault diagnosis. Currently, the turntable -factor wavelet transform (TQWT) was original proposed by Selesnick, and the advantage of TQWT is that the -factor is easily and continuously adjustable [16,17].…”

Section: Introductionmentioning

confidence: 99%

Bearing incipient fault diagnosis based upon maximal spectral kurtosis TQWT and group sparsity total variation denoising approach

Li¹,

Liang²

2018

J. vibroeng.

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Abstract. Localized faults in rolling bearing tend to result in periodic shocks and thus arouse periodic responses in the vibration signal. In this paper, a novel fault diagnosis method based on maximal spectral kurtosis tunable Q-factor wavelet transformation (TQWT) and group sparsity total variation denoising (GS-TVD) is proposed to address the issue of bearing incipient failure. Firstly, the range of Q-factor was pre-selected according to the spectral distribution of impulse component, and bearing vibration signal was transformed by the TQWT method. Then, the spectral kurtosis of each scale transform coefficients was calculated, and the optimal Q-factor and decomposition scale can be selected according to the kurtosis maximum principle. In order to remove the interference components and high-frequency noise from the reconstructed vibration signal generated by inverse TQWT, the GS-TVD approach is employed, thus the cyclic periodicity characteristic and transient impulses can be detected obviously. The two cases experimental results indicate that the proposed technique is more effective and applicable for bearing incipient fault diagnosis compared with traditional method.

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Roller bearing acoustic signature extraction by wavelet packet transform, applications in fault detection and size estimation

Cited by 137 publications

References 26 publications

A Comparison Study of Kernel Functions in the Support Vector Machine and Its Application for Termite Detection

A Comparison Study of Kernel Functions in the Support Vector Machine and Its Application for Termite Detection

Teager Energy Entropy Ratio of Wavelet Packet Transform and Its Application in Bearing Fault Diagnosis

Bearing incipient fault diagnosis based upon maximal spectral kurtosis TQWT and group sparsity total variation denoising approach

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