Quaternion singular spectrum analysis using convex optimization and its application to fault diagnosis of rolling bearing

Yi, Cancan; Lv, Yong; Dang, Zhang; Yu, Xun

doi:10.1016/j.measurement.2017.02.047

Cited by 77 publications

(39 citation statements)

References 28 publications

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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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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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“…As a result, the measured vibration signals are always overwhelmed by signals from multi-fault vibration sources and other measurement noise [9]. Consequently, there should be close attention paid to the theory of multi-fault signal analysis.…”

Section: Introductionmentioning

confidence: 99%

Research on the Blind Source Separation Method Based on Regenerated Phase-Shifted Sinusoid-Assisted EMD and Its Application in Diagnosing Rolling-Bearing Faults

Xiao

et al. 2017

Applied Sciences

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Abstract:To improve the performance of single-channel, multi-fault blind source separation (BSS), a novel method based on regenerated phase-shifted sinusoid-assisted empirical mode decomposition (RPSEMD) is proposed in this paper. The RPSEMD method is used to decompose the original single-channel vibration signal into several intrinsic mode functions (IMFs), with the obtained IMFs and original signal together forming a new observed signal for the dimensional lifting. Therefore, an undetermined problem is transformed into a positive definite problem. Compared with the existing EMD method and its improved version, the proposed RPSEMD method performs better in solving the mode mixing problem (MMP) by employing sinusoid-assisted technology. Meanwhile, it can also reduce the computational load and reconstruction errors. The number of source signals is estimated by adopting singular value decomposition (SVD) and Bayes information criterion (BIC). Simulation analysis has demonstrated the superiority of this method being applied in multi-fault BSS. Furthermore, its effectiveness in identifying the multi-fault features of rolling-bearing has been also verified based on a test rig.

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“…With this method, the pseudo low-frequency IMFs can be eliminated. Yi et al [9] proposed the augmented quaternion singular spectrum analysis multichannel denoising method. This method has a better ability than multivariate EMD method in multisignal processing.…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Signal Extraction Based on Stochastic Resonance-Based Denoising and VMD

Chen

2017

International Journal of Rotating Machinery

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Aiming at the difficulty of early fault vibration signal extraction of rolling bearing, a method of fault weak signal extraction based on variational mode decomposition (VMD) and quantum particle swarm optimization adaptive stochastic resonance (QPSO-SR) for denoising is proposed. Firstly, stochastic resonance parameters are optimized adaptively by using quantum particle swarm optimization algorithm according to the characteristics of the original fault vibration signal. The best stochastic resonance system parameters are output when the signal to noise ratio reaches the maximum value. Secondly, the original signal is processed by optimal stochastic resonance system for denoising. The influence of the noise interference and the impact component on the results is weakened. The amplitude of the fault signal is enhanced. Then the VMD method is used to decompose the denoised signal to realize the extraction of fault weak signals. The proposed method was applied in simulated fault signals and actual fault signals. The results show that the proposed method can reduce the effect of noise and improve the computational accuracy of VMD in noise background. It makes VMD more effective in the field of fault diagnosis. The proposed method is helpful to realize the accurate diagnosis of rolling bearing early fault.

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Quaternion singular spectrum analysis using convex optimization and its application to fault diagnosis of rolling bearing

Cited by 77 publications

References 28 publications

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

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

Research on the Blind Source Separation Method Based on Regenerated Phase-Shifted Sinusoid-Assisted EMD and Its Application in Diagnosing Rolling-Bearing Faults

Rolling Bearing Fault Signal Extraction Based on Stochastic Resonance-Based Denoising and VMD

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