Improved SNR to detect the unknown characteristic frequency by SR

Zhang, Jingling; Yang, Jianhua; Liu, Houguang

doi:10.1049/iet-smt.2018.0046

Cited by 7 publications

(2 citation statements)

References 27 publications

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“…For the actual fault characteristic frequency, there is little difference between the actual fault characteristic frequency and the theoretical fault characteristic frequency. According to our previous research results [37], it is also feasible to calculate the SNR at the actual fault characteristic frequency by using the theoretical fault characteristic frequency.…”

Section: Evaluation Indexmentioning

confidence: 99%

Distinguish coherence resonance and stochastic resonance in bearing fault evaluation

Yang¹,

Yang²,

Zhu³

et al. 2020

Meas. Sci. Technol.

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Similar to the famous stochastic resonance (SR), coherence resonance (CR) is an ordered dynamic behavior induced by the noise in a nonlinear system. Inspired by the dynamical characteristics of CR, the importance of distinguishing CR and SR in bearing fault evaluation is presented. It solves the problem that the characteristic frequency cannot be determined when using the SR for bearing fault diagnosis. The general scale transformation is introduced to process the vibration signal further to realize adaptive CR at different frequencies. The autocorrelation function and the resonance factor are key measurements of CR. Especially, the resonance factor index can reflect the output of the nonlinear system transforming from CR to SR and then realize the detection of the characteristic frequency and then identify the bearing fault. Numerical and experimental verifications show the advantages of the CR method in the periodic potential system in bearing fault diagnosis are illustrated. By distinguishing CR and SR successfully, we can improve the accuracy of bearing fault diagnosis.

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Section: Evaluation Indexmentioning

confidence: 99%

Distinguish coherence resonance and stochastic resonance in bearing fault evaluation

Yang¹,

Yang²,

Zhu³

et al. 2020

Meas. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, these time-domain indicators are not only easily affected by strong background noise and random pulses, but their output response is also greatly reduced. However, indices based on the frequency domain have been introduced to overcome the disadvantages of poor robustness, such as the classical signalto-noise ratio [41], improved SNR [42], spectral power amplification method [43], and weighted power spectral kurtosis indicators [44], etc. These indicators provide certain help for algorithm and system design; however, prior knowledge is not always feasible for signals with unknown characteristic frequencies and noise types in practical industrial engineering applications.…”

Section: Introductionmentioning

confidence: 99%

Weak signal enhancement for rolling bearing fault diagnosis based on adaptive optimized VMD and SR under strong noise background

Luo¹,

Wen²,

Lei³

et al. 2023

Meas. Sci. Technol.

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Owing to the nonlinearity and nonstationarity of the bearing fault signal, it is difficult to identify fault characteristics under the influence of a strong noise environment. The extraction of early weak fault features is critical for the reliability of bearing operations. Therefore, an urgent problem is reasonable noise reduction and feature enhancement in weak-signal processing. Traditional variational modal decomposition (VMD) and stochastic resonance (SR) are commonly applied to detect weak signals in fault diagnosis. The VMD method can decompose the signal into several intrinsic mode functions (IMFs) to effectively reduce the modal aliasing problem. However, uniform standards for the key parameters of decomposition and the selection of the optimal IMF after decomposition are lacking. Meanwhile, some disadvantages of SR still exist; for example, the interference of multiscale noise may lead to false detection by incorrect selection of high-pass filter parameters, and the system parameters are not adaptive to different signals to achieve the best response output. To better address the weak signal feature enhancement, a novel rolling bearing fault diagnosis method combining adaptive VMD and SR by improved differential search (IDS) optimization is proposed. First, the bearing fault signal is decomposed into several IMFs using the IDS-VMD. Second, the feature information of the fault signal is retained and reconstructed using the correlation kurtosis for sensitive modal extraction. Furthermore, the fault features of the reconstructed signal are effectively enhanced by the variable-step IDS-SR, which can reasonably transfer the noise energy of the input components to the fault characteristic frequency. Finally, the periodic pulse can be observed in the corresponding envelope spectrum. The simulated and experimental data show that the proposed method can not only effectively extract the signal feature information in the actual fault but also realize early weak fault diagnosis of rolling bearings more accurately.

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Stochastic resonance with reinforcement learning for underwater acoustic communication signal

Qiu

Yuan

et al. 2021

Applied Acoustics

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Improved SNR to detect the unknown characteristic frequency by SR

Cited by 7 publications

References 27 publications

Distinguish coherence resonance and stochastic resonance in bearing fault evaluation

Distinguish coherence resonance and stochastic resonance in bearing fault evaluation

Weak signal enhancement for rolling bearing fault diagnosis based on adaptive optimized VMD and SR under strong noise background

Stochastic resonance with reinforcement learning for underwater acoustic communication signal

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