Signal feature extraction based on cascaded multi-stable stochastic resonance denoising and EMD method

Shi, Pengfei; An, Shujun; Pei, Li; Han, Dongying

doi:10.1016/j.measurement.2016.04.073

Cited by 67 publications

(34 citation statements)

References 23 publications

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“…The value of , and determine the potential function which belong to multi-stable, bistable or monostable system. In this paper, the potential function belongs to the multi-stable system [29]. The Eq.…”

Section: The Principles Of Multi-stable Sr Modelmentioning

confidence: 99%

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A new weak fault diagnosis method based on multi-scale wavelet noise tuning cascaded multi-stable stochastic resonance

Han¹,

An²,

Shi³

et al. 2018

J. vibroeng.

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For detecting the weak fault diagnosis submerged in heavy noise, a new method called multi-scale cascaded multi-stable stochastic resonance (MCMSR) is studied. The method can effectively extract weak fault diagnosis from noise background using multi-scale wavelet noise tuning stochastic resonance (SR). Firstly, input signal with noise is decomposed by multi-scale wavelets transformation, and each scale signal is adjusted by scaling factor, then the decomposed signal is used as the input of cascaded multi-stable systems to achieve the detection of fault diagnosis. If the input signal is a large parameter signal, to conform to the conditions of SR, the decomposed signal must be processed by twice sampling. The simulation and experimental signals are carried out to test the feasibility of the method. From the signal to noise ratio (SNR) comparison curves of original signal, SR output signal and MCMSR output signal plotted together, we can find that the useful signal can be enhanced by MCMSR method than SR method. The experimental results indicate that the MCMSR can extract fault diagnosis from heavy background noise.

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Section: The Principles Of Multi-stable Sr Modelmentioning

confidence: 99%

“…Although SR has a distinct advantage in the weak signal processing, when the SNR of vibration signals is low, the detection effect of single SR is not satisfactory [25]. To obtain higher SNR, the Cascaded Stochastic Resonance (CSR) was proposed to increase the output SNR [26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

A new weak fault diagnosis method based on multi-scale wavelet noise tuning cascaded multi-stable stochastic resonance

Han¹,

An²,

Shi³

et al. 2018

J. vibroeng.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Established techniques include wavelet transform [6], timefrequency distribution (TFD) [7], Hilbert-Huang transform (HHT) [8], blind source separation [9], and multivariate statistical analysis [10]. In summary, signal detection technology can be divided into two concepts [11][12][13]. The first involves obtaining a useful signal by eliminating or suppressing noise.…”

Section: Introductionmentioning

confidence: 99%

Weak Fault Signal Detection of Rotating Machinery Based on Multistable Stochastic Resonance and VMD‐AMD

Han

Shi

2018

Shock and Vibration

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For solving detection problems of multifrequency weak signals in noisy background, a novel weak signal detection method based on variational mode decomposition (VMD) and rescaling frequency-shifted multistable stochastic resonance (RFMSR) with analytical mode decomposition (AMD) is proposed. In this method, different signal frequency bands are processed by rescaling subsampling compression to make each frequency band meet the conditions of stochastic resonance. Before the enhanced signal components are synthesized, they are processed to achieve the enhanced signal by means of AMD, leaving only the enhanced sections of the signal. The processed signal is decomposed into intrinsic mode functions (IMF) by VMD, in order to require the detection of weak multifrequency signals. The experimental analysis of the rolling bearing inner ring fault and gear fault diagnosis demonstrate that the proposed method can not only enhance signal amplitude, reduce false components, and improve the VMD algorithm's accuracy, but also effectively detect weak multifrequency signals submerged by noise.

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“…Shi et al [27] proposed a method of empirical mode decomposition based on a cascaded multi-stable stochastic resonance system. Wang et al [28] proposed a novel faint signal extraction method by a time-frequency distribution image dimensionality reduction in order to overcome the shortcomings of the fast Kurtogram method.…”

Section: Introductionmentioning

confidence: 99%

A New Feature Extraction Method Based on EEMD and Multi-Scale Fuzzy Entropy for Motor Bearing

Zhao

Sun

Deng

et al. 2016

Entropy

183

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Abstract:Feature extraction is one of the most important, pivotal, and difficult problems in mechanical fault diagnosis, which directly relates to the accuracy of fault diagnosis and the reliability of early fault prediction. Therefore, a new fault feature extraction method, called the EDOMFE method based on integrating ensemble empirical mode decomposition (EEMD), mode selection, and multi-scale fuzzy entropy is proposed to accurately diagnose fault in this paper. The EEMD method is used to decompose the vibration signal into a series of intrinsic mode functions (IMFs) with a different physical significance. The correlation coefficient analysis method is used to calculate and determine three improved IMFs, which are close to the original signal. The multi-scale fuzzy entropy with the ability of effective distinguishing the complexity of different signals is used to calculate the entropy values of the selected three IMFs in order to form a feature vector with the complexity measure, which is regarded as the inputs of the support vector machine (SVM) model for training and constructing a SVM classifier (EOMSMFD based on EDOMFE and SVM) for fulfilling fault pattern recognition. Finally, the effectiveness of the proposed method is validated by real bearing vibration signals of the motor with different loads and fault severities. The experiment results show that the proposed EDOMFE method can effectively extract fault features from the vibration signal and that the proposed EOMSMFD method can accurately diagnose the fault types and fault severities for the inner race fault, the outer race fault, and rolling element fault of the motor bearing. Therefore, the proposed method provides a new fault diagnosis technology for rotating machinery.

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Signal feature extraction based on cascaded multi-stable stochastic resonance denoising and EMD method

Cited by 67 publications

References 23 publications

A new weak fault diagnosis method based on multi-scale wavelet noise tuning cascaded multi-stable stochastic resonance

A new weak fault diagnosis method based on multi-scale wavelet noise tuning cascaded multi-stable stochastic resonance

Weak Fault Signal Detection of Rotating Machinery Based on Multistable Stochastic Resonance and VMD‐AMD

A New Feature Extraction Method Based on EEMD and Multi-Scale Fuzzy Entropy for Motor Bearing

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