Application of an improved maximum correlated kurtosis deconvolution method for fault diagnosis of rolling element bearings

Miao, Yongwu; Zhao, Ming; Lin, Jing; Lei, Yaguo

doi:10.1016/j.ymssp.2017.01.033

Cited by 266 publications

(124 citation statements)

References 36 publications

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“…However, some fluctuation exists in the success rates of EFS at multirun simulation. As reported in 4 Complexity Table 2, the statistical values (means and variances denoted in equations (17) and (18)) also prove that the proposed method has a good stability for indicating the ICF of latent faulty mode.…”

Section: Simulation Studysupporting

confidence: 56%

“…In recent decades, various methods based on vibration signal have been introduced by researchers for the diagnosis of faulty rolling bearings, such as deconvolution analysis [4,5], wavelet transform [6], spectral kurtosis-based method [7][8][9][10], time-frequency analysis [11,12], and the adaptive signal decomposition methods [13,14]. Among these methods, the adaptive signal decomposition methods have attracted more and more attention and are the hot topic for bearing fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

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An Enhanced VMD with the Guidance of Envelope Negentropy Spectrum for Bearing Fault Diagnosis

et al. 2020

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Currently, study on the relevant methods of variational mode decomposition (VMD) is mainly focused on the selection of the number of decomposed modes and the bandwidth parameter using various optimization algorithms. Most of these methods utilize the genetic-like algorithms to quantitatively analyze these parameters, which increase the additional initial parameters and inevitably the computational burden due to ignoring the inherent characteristics of the VMD. From the perspective to locate the initial center frequency (ICF) during the VMD decomposition process, we propose an enhanced VMD with the guidance of envelope negentropy spectrum for bearing fault diagnosis, thus effectively avoiding the drawbacks of the current VMD-based algorithms. First, the ICF is coarsely located by envelope negentropy spectrum (ENS) and the fault-related modes are fast extracted by incorporating the ICF into the VMD. Then, the fault-related modes are adaptively optimized by adjusting the bandwidth parameters. Lastly, in order to identify fault-related features, the Hilbert envelope demodulation technique is used to analyze the optimal mode obtained by the proposed method. Analysis results of simulated and experimental data indicate that the proposed method is effective to extract the weak faulty characteristics of bearings and has advantage over some advanced methods. Moreover, a discussion on the extension of the proposed method is put forward to identify multicomponents for broadening its applied scope.

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Section: Simulation Studysupporting

confidence: 56%

Section: Introductionmentioning

confidence: 99%

An Enhanced VMD with the Guidance of Envelope Negentropy Spectrum for Bearing Fault Diagnosis

et al. 2020

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“…To evaluate the performance of the proposed method, according to the proposed vibration model of a rolling bearing [35,36], the synthetic signal x(t) is generated by:…”

Section: Numerical Experimentsmentioning

confidence: 99%

Sparse Envelope Spectra for Feature Extraction of Bearing Faults Based on NMF

Shan

Liu

et al. 2019

Applied Sciences

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Periodic impulses and the oscillation response signal are the vital feature indicators of rolling bearing faults. However, finding the suitable feature frequency band is usually difficult due to the interferences of other components and multiple resonance regions. According to the characteristics of non-negative matrix factorization (NMF) on a spectrogram, the feature extraction method from a sparse envelope spectrum for rolling bearing faults is proposed in this paper. On the basis of the time–frequency distribution (TFD) of the periodic transient oscillations, the basic matrix can be interpreted as the spectral bases, and the time weight matrix corresponding to spectral bases can be extracted by NMF. Because the bases and the weights have a one-to-one correspondence, the frequency band filtering with the basic component and the time domain envelope of the weight vector are calculated respectively. Then, the sparse envelope spectrum can be derived by the inner product of the above results. The effectiveness of the proposed method is verified by simulations and experiments. Compared with band-pass filtering and spectral kurtosis methods, and considering the time weights and corresponding the spectral bases for the periodic transient oscillations, the weak fault-rated feature can be enhanced in the sparse spectrum, while other components and noise are weakened. Therefore, the proposed method can reduce the requirement of selecting frequency band filtering.

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“…In order to demonstrate the proposed method, we use the similar simulated bearing fault signals according to the vibration model in [23]:…”

Section: Simulation Analysismentioning

confidence: 99%

Fault diagnosis of rolling element bearings with a spectrum searching method

Qiu

Zhu

et al. 2017

Meas. Sci. Technol.

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Rolling element bearing faults in rotating systems are observed as impulses in the vibration signals, which are usually buried in noises. In order to effectively detect the fault of bearings, a novel spectrum searching method is proposed. The structural information of spectrum (SIOS) on a predefined frequency grid is constructed through a searching algorithm, such that the harmonics of impulses generated by faults can be clearly identified and analysed. Local peaks of the spectrum are projected onto certain components of the frequency grid, and then the SIOS can interpret the spectrum via the number and power of harmonics projected onto components of the frequency grid. Finally bearings can be diagnosed based on the SIOS by identifying its dominant or significant components. Mathematical formulation is developed to guarantee the correct construction of the SIOS through searching. The effectiveness of the proposed method is verified with simulated signals and experimental signals.

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Application of an improved maximum correlated kurtosis deconvolution method for fault diagnosis of rolling element bearings

Cited by 266 publications

References 36 publications

An Enhanced VMD with the Guidance of Envelope Negentropy Spectrum for Bearing Fault Diagnosis

An Enhanced VMD with the Guidance of Envelope Negentropy Spectrum for Bearing Fault Diagnosis

Sparse Envelope Spectra for Feature Extraction of Bearing Faults Based on NMF

Fault diagnosis of rolling element bearings with a spectrum searching method

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