A novel blind deconvolution method and its application to fault identification

Cheng, Yao; Chen, Bingyan; Mei, Guiming; Wang, Zhiwei; Zhang, Weihua

doi:10.1016/j.jsv.2019.114900

Cited by 66 publications

(59 citation statements)

References 30 publications

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“…Particle swarm optimization (PSO) is a swarm intelligence algorithm with simple operation and fast convergence. 28–30 It is used to adaptively select the optimal combination of α and ω d by using the following objective function…”

Section: Optimized Variational Mode Extractionmentioning

confidence: 99%

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An optimized variational mode extraction method for rolling bearing fault diagnosis

Pang

Nazari

Sun

et al. 2021

Structural Health Monitoring

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The fault feature signal of rolling bearing can be characterized as the narrow-band signal with a specific resonance frequency. Therefore, resonance demodulation analysis is a powerful damage detection technique of bearings. In addition to the fault feature signal, the measured vibration signals carry various interference components, and these interference components become a serious obstacle of fault feature extraction. Variational mode extraction is a novel signal analysis method designed to retrieve a specific signal component from the composite signal. Variational mode extraction is founded on a similar basis as variational mode decomposition, while it shows better accuracy and higher efficiency compared with variational mode decomposition. In this study, variational mode extraction is introduced to the resonance demodulation analysis of bearing fault. As the results of variational mode extraction analysis are greatly influenced by the choice of two parameters, that is, the balancing factor α and the initial guess of center frequency ωd, an optimized variational mode extraction method is further developed. First, a new fault information evaluation index for measuring the richness of fault characteristics of the signal, termed ensemble impulsiveness and cyclostationarity, is formulated. Second, the ensemble impulsiveness and cyclostationarity is used as the fitness function of particle swarm optimization to automatically determine the optimal values of α and ωd. Finally, the validity of optimized variational mode extraction method is verified by simulated and experimental analysis, and the superiority of optimized variational mode extraction method is highlighted through comparison with two other advanced resonance demodulation analysis approaches, that is, the improved kurtogram and infogram. The analysis results indicate that optimized variational mode extraction method has a powerful capability of resonance demodulation analysis.

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Section: Optimized Variational Mode Extractionmentioning

confidence: 99%

“…Figure 12(a) shows the experiment stand. The rolling element defect is seeded artificially with a width of 0.2 mm and a depth of 0.6 mm, 29 as displayed in Figure 12(b). Table 2 lists the details of the damaged bearing.…”

Section: Experiments Verificationmentioning

confidence: 99%

An optimized variational mode extraction method for rolling bearing fault diagnosis

Pang

Nazari

Sun

et al. 2021

Structural Health Monitoring

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“…Parameters for the simulated signal. (CK), 40 of different kernels are presented in Table 2. It can be seen that the IGPR reaches the highest values whether r or CK, which indicates whose IAS estimation accuracy is far larger than other kernels.…”

Section: Simulation Analysismentioning

confidence: 99%

“…For quantitative evaluation of the performance of the proposed method in IAS estimation, the Pearson’s correlation coefficient ρ , and correlated Kurtosis ( CK ), 40 of different kernels are presented in Table 2. It can be seen that the IGPR reaches the highest values whether ρ or CK , which indicates whose IAS estimation accuracy is far larger than other kernels.…”

Section: Simulation Analysismentioning

confidence: 99%

Encoder-based weak fault detection for rotating machinery using improved Gaussian process regression

Zhao

Chen

et al. 2020

Structural Health Monitoring

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Encoder signal analysis has proven to be a novel and cost-effective tool for the health monitoring of rotating machinery. Nevertheless, how to effectively detect the potential fault utilizing encoder information, especially at an early stage, remains a challenging issue. In light of this limitation, an improved Gaussian process regression analysis is proposed for the weak fault detection of rotating machinery via encoder signal. In this article, the Gaussian process regression model is first introduced to estimate the instantaneous angular speed and its confidence interval. Subsequently, to improve the robustness of Gaussian process regression under weak fault conditions, a spectral density complex kernel is constructed through modeling the spectral density with a mixture of Gaussians. Finally, built upon the eigenvalue decomposition, the optimal inference approach of improved Gaussian process regression is proposed. Compared with other regression methods, the major contribution is that the new method not only enhances the weak fault-related features but also sets their confidence interval adaptively. Using the proposed improved Gaussian process regression, the interference components are suppressed, while the fault-related instantaneous angular speed outliers are accurately detected. In addition, the significance of fault can be quantitatively evaluated according to the confidence level of the improved Gaussian process regression. The simulated and experimental analyses manifest that the proposed improved Gaussian process regression method can effectively identify the early weak fault. It may offer an effective tool for early fault detection of rotating machinery in industrial applications.

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“…If they are not detected in the early stage, the local defects could lead to significant economic losses and even catastrophes [5]. erefore, fault detection and diagnosis of bearings play an important role in maintaining the availability and reliability of the mechanical system during the past decades [6,7].…”

Section: Introductionmentioning

confidence: 99%

Multistage Fault Feature Extraction of Consistent Optimization for Rolling Bearings Based on Correlated Kurtosis

Long

Cai

Xiong

et al. 2020

Shock and Vibration

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Fault diagnosis of rolling bearings is not a trivial task because fault-induced periodic transient impulses are always submerged in environmental noise as well as large accidental impulses and attenuated by transmission path. In most hybrid diagnostic methods available for rolling bearings, the problems lie in twofolds. First, most optimization indices used in the individual signal processing stage do not take the periodical characteristic of fault transient impulses into consideration. Second, the individual stages make use of different optimization indices resulting in inconsistent optimization directions and possibly an unsatisfied diagnosis. To solve these problems, a multistage fault feature extraction method of consistent optimization for rolling bearings based on correlated kurtosis (CK) is proposed where maximum correlated kurtosis deconvolution (MCKD) is employed to attenuate the influence of transmission path followed by tunable Q factor wavelet transform (TQWT) to further enhance fault features by decomposing the preprocessed signals into multiple subbands under different Q values. The major contribution of the proposed approach is to consistently use CK as an optimization index of both MCKD and TQWT. The subband signal with the maximum CK which is an index being able to measure the periodical transient impulses in vibration signal yields an envelope spectrum, from which fault diagnosis is implemented. Simulated and experimental signals verified the effectiveness and advantages of the proposed method.

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A novel blind deconvolution method and its application to fault identification

Cited by 66 publications

References 30 publications

An optimized variational mode extraction method for rolling bearing fault diagnosis

An optimized variational mode extraction method for rolling bearing fault diagnosis

Encoder-based weak fault detection for rotating machinery using improved Gaussian process regression

Multistage Fault Feature Extraction of Consistent Optimization for Rolling Bearings Based on Correlated Kurtosis

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