Approach to Weak Signal Extraction Based on Empirical Mode Decomposition and Stochastic Resonance

2013 ◽  
Vol 819 ◽  
pp. 216-221
Author(s):  
Pan Zhang ◽  
Tai Yong Wang ◽  
Lu Liu ◽  
Lu Yang Jin ◽  
Jin Xiang Fang
Keyword(s):  
Stochastic Resonance ◽  
Empirical Mode Decomposition ◽  
Signal Extraction ◽  
Weak Signal ◽  
Weak Signals ◽  
Mode Decomposition ◽  
Pre Treatment

The empirical mode decomposition (EMD) of weak signals submerged in a heavy noise was conducted and a method of stochastic resonance (SR) used for noisy EMD was presented. This method used SR as pre-treatment of EMD to remove noise and detect weak signals. The experiment result prove that this method, compared with that using EMD directly, not only improve SNR, enhance weak signals, but also improve the decomposition performance and reduce the decomposition layers.

scholarly journals Fault Diagnosis Using Cascaded Adaptive Second-Order Tristable Stochastic Resonance and Empirical Mode Decomposition

2021 ◽  
Vol 11 (23) ◽  
pp. 11480
Author(s):  
Hongjiang Cui ◽  
Ying Guan ◽  
Wu Deng
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Empirical Mode Decomposition ◽  
Low Frequency ◽  
Second Order ◽  
Weak Signal ◽  
Ant Colony Optimization Algorithm ◽  
Resonance Theory ◽  
Mode Decomposition ◽  
Diagnosis Method

Aiming at the problems of poor decomposition quality and the extraction effect of a weak signal with strong noise by empirical mode decomposition (EMD), a novel fault diagnosis method based on cascaded adaptive second-order tristable stochastic resonance (CASTSR) and EMD is proposed in this paper. In the proposed method, low-frequency interference components are filtered by using high-pass filtering, and the restriction conditions of stochastic resonance theory are solved by using an ordinary variable-scale method. Then, a chaotic ant colony optimization algorithm with a global optimization ability is employed to adaptively adjust the parameters of the second-order tristable stochastic resonance system to obtain the optimal stochastic resonance, and noise reduction pretreatment technology based on CASTSR is developed to enhance the weak signal characteristics of low frequency. Next, the EMD is employed to decompose the denoising signal and extract the characteristic frequency from the intrinsic mode function (IMF), so as to realize the fault diagnosis of rolling bearings. Finally, the numerical simulation signal and actual bearing fault data are selected to prove the validity of the proposed method. The experiment results indicate that the proposed fault diagnosis method can enhance the decomposition quality of the EMD, effectively extract features of weak signals, and improve the accuracy of fault diagnosis. Therefore, the proposed fault diagnosis method is an effective fault diagnosis method for rotating machinery.

scholarly journals Weak Signal Enhance Based on the Neural Network Assisted Empirical Mode Decomposition

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3373
Author(s):  
Kai Chen ◽  
Kai Xie ◽  
Chang Wen ◽  
Xin-Gong Tang
Keyword(s):  
Neural Network ◽  
Empirical Mode Decomposition ◽  
Short Term Memory ◽  
Ensemble Empirical Mode Decomposition ◽  
Generative Adversarial Networks ◽  
Natural Mode ◽  
Weak Signal ◽  
Weak Signals ◽  
Mode Decomposition ◽  
Enhancement Method

In order to enhance weak signals in strong noise background, a weak signal enhancement method based on EMDNN (neural network-assisted empirical mode decomposition) is proposed. This method combines CEEMD (complementary ensemble empirical mode decomposition), GAN (generative adversarial networks) and LSTM (long short-term memory), it enhances the efficiency of selecting effective natural mode components in empirical mode decomposition, thus the SNR (signal-noise ratio) is improved. It can also reconstruct and enhance weak signals. The experimental results show that the SNR of this method is improved from 4.1 to 6.2, and the weak signal is clearly recovered.

An adaptive empirical mode decomposition and stochastic resonance system in high efficient detection of terahertz radar signal

2020 ◽  
Vol 563 (1) ◽  
pp. 148-160
Author(s):  
Shan Wang ◽  
Pingjuan Niu ◽  
Qinghua Guo ◽  
Xiaochao Wang ◽  
Fuzhong Wang
Keyword(s):  
Stochastic Resonance ◽  
Empirical Mode Decomposition ◽  
Radar Signal ◽  
Resonance System ◽  
Efficient Detection ◽  
Mode Decomposition ◽  
High Efficient

scholarly journals Fault Diagnosis of Rotating Machinery Based on Adaptive Stochastic Resonance and AMD-EEMD

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Peiming Shi ◽  
Cuijiao Su ◽  
Dongying Han
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Empirical Mode Decomposition ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Rotating Machinery ◽  
Good Effect ◽  
Resonance System ◽  
Mode Decomposition ◽  
Eemd Method

An adaptive stochastic resonance and analytical mode decomposition-ensemble empirical mode decomposition (AMD-EEMD) method is proposed for fault diagnosis of rotating machinery in this paper. Firstly, the stochastic resonance system is optimized by particle swarm optimization (PSO), and the best structure parameters are obtained. Then, the signal with noise is put into the stochastic resonance system and denoising and enhancing the signal. Secondly, the signal output from the stochastic resonance system is extracted by analytical mode decomposition (AMD) method. Finally, the signal is decomposed by ensemble empirical mode decomposition (EEMD) method. The simulation results show that the optimal stochastic resonance system can effectively improve the signal-to-noise ratio, and the number of effective components of EEMD decomposition is significantly reduced after using AMD, thus improving the decomposition results of EEMD and enhancing the amplitude of components frequency. Through the extraction of the rolling bearing fault signal feature proved that the method has a good effect.

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