scholarly journals Adaptive Unsaturated Bistable Stochastic Resonance Multi-Frequency Signals Detection Based on Preprocessing

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2055
Author(s):  
Lin Cui ◽  
Junan Yang ◽  
Lunwen Wang ◽  
Hui Liu
Keyword(s):  
Stochastic Resonance ◽  
Background Noise ◽  
Detection Method ◽  
Single Frequency ◽  
Complex Signal ◽  
Weak Signals ◽  
Signal Energy ◽  
Mode Decomposition ◽  
Noise Energy ◽  
Periodic Signals

Stochastic resonance (SR) has been widely used for extracting single-frequency weak periodic signals. For multi-frequency weak signals, empirical mode decomposition (EMD) can adaptively decompose the complex signal, but this method also suffers from mode mixing, which affects the accuracy of detection. SR can convert part of the noise energy into signal energy, which compensates for the defects of EMD. According to the advantages of SR and EMD, we constructed a multi-frequency signals detection method using adaptive unsaturated bistable SR based on EMD (EMD-AUBSR). In this study, we avoid the inherent saturation of SR by reconstructing the potential function and improve the multi-frequency signals detection ability by adding the preprocessing element. For strong background noise, the experimental results show that this proposed can effectively detect multi-frequency weak signals and decrease signal aliasing, whereas EMD alone cannot.

scholarly journals Study of the Method of Multi-Frequency Signal Detection Based on the Adaptive Stochastic Resonance

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Zhenyu Lu ◽  
Tingya Yang ◽  
Min Zhu
Keyword(s):  
Stochastic Resonance ◽  
Resonance Effect ◽  
Resonance Phenomenon ◽  
Detection Methods ◽  
Single Frequency ◽  
Measured Signal ◽  
Weak Signals ◽  
System Parameters ◽  
Input Signals ◽  
Periodic Signals

Recently, the stochastic resonance effect has been widely used by the method of discovering and extracting weak periodic signals from strong noise through the stochastic resonance effect. The detection of the single-frequency weak signals by using stochastic resonance effect is widely used. However, the detection methods of the multifrequency weak signals need to be researched. According to the different frequency input signals of a given system, this paper puts forward a detection method of multifrequency signal by using adaptive stochastic resonance, which analyzed the frequency characteristics and the parallel number of the input signals, adjusted system parameters automatically to the low frequency signals in the fixed step size, and then measured the stochastic resonance phenomenon based on the frequency of the periodic signals to select the most appropriate indicators in the middle or high frequency. Finally, the optimized system parameters are founded and the frequency of the given signals is extracted in the frequency domain of the stochastic resonance output signals. Compared with the traditional detection methods, the method in this paper not only improves the work efficiency but also makes it more accurate by using the color noise, the frequency is more accurate being extracted from the measured signal. The consistency between the simulation results and analysis shows that this method is effective and feasible.

A Novel Adaptive Stochastic Resonance Method Based on Tristable System and its Applications

2020 ◽  
pp. 2150004
Author(s):  
Gang Zhang ◽  
Chuan Jiang ◽  
Tian Qi Zhang
Keyword(s):  
Signal Detection ◽  
Stochastic Resonance ◽  
Piecewise Linear ◽  
Correlation Coefficients ◽  
Resonance Method ◽  
Resonance System ◽  
Signal Energy ◽  
Noise Energy ◽  
Novel Model ◽  
Output Snr

Stochastic resonance systems have the advantages of converting noise energy into signal energy, and have great potential in the field of signal detection and extraction. Aiming at the problems of the performance of classical stochastic resonance system whose model is not perfect enough and the correlation coefficients between parameters is too large to be optimized by algorithm, then a novel model of the tristable potential stochastic resonance system is proposed. The output SNR formula of the model is derived and analyzed, and the influence of its parameters on the model is clarified. Compared with the piecewise linear model by numerical simulation, the correctness of the formula and the superiority of the model are verified. Finally, the model and the classical tristable model are applied to bearing fault detection in which the genetic algorithm is used to optimize the parameters of the two systems. The results show that the model has better detection effects, which prove that the model has a strong potential in the field of signal detection.

scholarly journals A Periodic Potential Underdamped Stochastic Resonance Method and Its Application for Gear Fault Diagnosis

2019 ◽  
Author(s):  
Zhixing Li ◽  
Xiandong Liu ◽  
Tian He ◽  
Yingchun Shan
Keyword(s):  
Stochastic Resonance ◽  
Background Noise ◽  
Ant Colony Algorithm ◽  
Periodic Potential ◽  
Potential Model ◽  
Signal To Noise Ratio ◽  
Resonance Method ◽  
Noise Energy ◽  
Gear Fault ◽  
Potential Structure

The vibration feature of weak gear fault is often covered in strong background noise, which makes it necessary to establish weak feature enhancement methods. Among the enhancement methods, stochastic resonance (SR) has the unique advantage of transferring noise energy to weak signals and has a great application prospection in weak signal extraction. But the traditional SR potential model cannot form a richer potential structure and may lead to system instability when the noise is too great. To overcome these shortcomings, the article presents a periodic potential underdamping stochastic resonance (PPUSR) method after investigating the potential function and system signal-to-noise ratio (SNR). In addition, system parameters are further optimized by using ant colony algorithm. Through simulation and gear experiments, the effectiveness of the proposed method was verified. We concluded that compared with the traditional underdamped stochastic resonance (TUSR) method, the PPUSR method had a higher recognition degree and better frequency response capability.

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.

Study on Vibration Signal Marginal Spectrum of Paper-Transferring System in Printing Press Based on EMD

2010 ◽  
Vol 139-141 ◽  
pp. 2464-2468
Author(s):  
Yi Ming Wang ◽  
Shao Hua Zhang ◽  
Zhi Hong Zhang ◽  
Jing Li
Keyword(s):  
Empirical Mode Decomposition ◽  
Optimization Design ◽  
Impact Load ◽  
Vibration Signal ◽  
Key Factors ◽  
Mode Function ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Periodic Signals ◽  
The Moment

The precision of transferring paper is key factors to decide the print overprint accuracy, and vibration has an important impact on paper transferring accuracy. Empirical mode decomposition (EMD) can be used to extract the features of vibration test signal. According to the intrinsic mode function (IMF) by extracted, it is useful to analyze the dynamic characteristics of swing gripper arm on motion state. Due to the actual conditions of printing, the vibration signal of Paper-Transferring mechanism system is complex quasi periodic signals. Hilbert-Huang marginal spectrum that is based on empirical mode decomposition can solve the problem which is modals leakage by FFT calculated in frequency domain. Through the experimental research, the phase information of impact load at the moment of grippers opening or closing, which can be used for the optimization design of Paper-Transferring system and the improvement in the accuracy of swing gripper arm.

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