Early fault feature extraction of rolling bearing based on ICD and tunable Q-factor wavelet transform

Li, Yongbo; Liang, Xihui; Xu, Min; Huang, Wenhu

doi:10.1016/j.ymssp.2016.10.013

Cited by 110 publications

(53 citation statements)

References 27 publications

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“…Also, according to Equation (23), the fault frequency of the out racer of the bearing can be computed, which is about 236 HZ [29]. Based on this prior fault frequency, the ACFHNR index value of all the fault vibration signals in the whole life of the testing are calculated, as shown in Figure 17a.…”

Section: Experiments Resultsmentioning

confidence: 99%

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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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Section: Experiments Resultsmentioning

confidence: 99%

“…A variety of methods to decompose the fault signal for extracting the weak periodic impulse feature were proposed such as EEMD [12,13], LMD [14], intrinsic characteristic-scale decomposition (ICD) [29]. However, how to choose the sensitive feature components remains a problem to be solved.…”

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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“…In this method, the EEMD is applied to decompose the vibration signals into a series of IMFs and then the TQWT is employed to separate the main IMF into high -factor component and low -factor component to diagnose the early fault. Furthermore, an improvement works has been aroused in applying intrinsic characteristic-scale decomposition (ICD) and TQWT for fault diagnosis of rolling bearings [19]. However, there are two main shortcomings in the above methods:…”

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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“…The feature extracted from vibration signal is commonly used to detect faults in machines [2]. And the more meaningful feature can enhance the identification accuracy.…”

Section: Introductionmentioning

confidence: 99%

An automatic feature extraction method and its application in fault diagnosis

Wang¹,

Li²,

Jiang³

et al. 2017

J. vibroeng.

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The main challenge of fault diagnosis is to extract excellent fault feature, but these methods usually depend on the manpower and prior knowledge. It is desirable to automatically extract useful feature from input data in an unsupervised way. Hence, an automatic feature extraction method is presented in this paper. The proposed method first captures fault feature from the raw vibration signal by sparse filtering. Considering that the learned feature is high-dimensional data which cannot achieve visualization, -distributed stochastic neighbor embedding ( -SNE) is further selected as the dimensionality reduction tool to map the learned feature into a three-dimensional feature vector. Consequently, the effectiveness of the proposed method is verified using gearbox and bearing experimental datas. The classification results show that the hybrid method of sparse filtering and -SNE can well extract discriminative information from the raw vibration signal and can clearly distinguish different fault types. Through comparison analysis, it is also validated that the proposed method is superior to the other methods.

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Early fault feature extraction of rolling bearing based on ICD and tunable Q-factor wavelet transform

Cited by 110 publications

References 27 publications

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

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

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

An automatic feature extraction method and its application in fault diagnosis

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