Weak impulsive signals detection based on step-varying asymmetric stochastic resonance

2016 ◽  
Vol 231 (2) ◽  
pp. 242-262 ◽  
Author(s):  
Haibin Zhang ◽  
Yuan Zheng ◽  
Fanrang Kong
Keyword(s):  
Stochastic Resonance ◽  
High Frequency ◽  
Signal To Noise Ratio ◽  
Signal Denoising ◽  
Resonance System ◽  
Rotating Machine ◽  
Frequency Components ◽  
Optimal Dynamic ◽  
The Asymmetry ◽  
Fault Information

Rotating machinery response is often characterized by the presence of periodic impulses modulated by high-frequency components. The fault information is often hidden in its envelope signal which is unilateral when demodulated. Conventional stochastic resonance with a symmetric potential cannot always contain the signal’s original features especially the asymmetry. In this article, a step-varying asymmetric stochastic resonance system for impulsive signal denoising and recovery as well as the rotating machine fault diagnosis is proposed to further improve the impulsive signal-to-noise ratio. In the method, the asymmetry of step-varying asymmetric stochastic resonance can match the unilateral impulsive signal well to generate an optimal dynamic system by selecting proper system parameters and degree of asymmetry. Systems with different simulated or experimental signals are also studied to verify its effectiveness and availability. Results indicate that the step-varying asymmetric stochastic resonance performs much better in detection of impulsive signal than the conventional stochastic resonance with merits of good frequency response, anti-noise capability, adaptability to asymmetric signal and original waveform preserving.

On representing noise by deterministic excitations for interpreting the stochastic resonance phenomenon

2021 ◽  
Vol 379 (2192) ◽  
pp. 20200229 ◽  
Author(s):  
V. Sorokin ◽  
I. Demidov
Keyword(s):  
Stochastic Resonance ◽  
High Frequency ◽  
Stochastic Systems ◽  
Signal To Noise Ratio ◽  
Nonlinear Damping ◽  
Resonance Phenomenon ◽  
Oscillatory Systems ◽  
Stochastic Excitations ◽  
Viable Approach ◽  
Stochastic Resonance Phenomenon

Adding noise to a system can ‘improve’ its dynamic behaviour, for example, it can increase its response or signal-to-noise ratio. The corresponding phenomenon, called stochastic resonance, has found numerous applications in physics, neuroscience, biology, medicine and mechanics. Replacing stochastic excitations with high-frequency ones was shown to be a viable approach to analysing several linear and nonlinear dynamic systems. For these systems, the influence of the stochastic and high-frequency excitations appears to be qualitatively similar. The present paper concerns the discussion of the applicability of this ‘deterministic’ approach to stochastic systems. First, the conventional nonlinear bi-stable system is briefly revisited. Then dynamical systems with multiplicative noise are considered and the validity of replacing stochastic excitations with deterministic ones for such systems is discussed. Finally, we study oscillatory systems with nonlinear damping and analyse the effects of stochastic and deterministic excitations on such systems. This article is part of the theme issue ‘Vibrational and stochastic resonance in driven nonlinear systems (part 1)’.

scholarly journals Analysis of Asymmetric Piecewise Linear Stochastic Resonance Signal Processing Model Based on Genetic Algorithm

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Lina He ◽  
Chuan Jiang
Keyword(s):  
Genetic Algorithm ◽  
Fault Detection ◽  
Stochastic Resonance ◽  
Signal To Noise Ratio ◽  
Piecewise Linear ◽  
Poor Performance ◽  
System Model ◽  
Adaptive Genetic Algorithm ◽  
Resonance System ◽  
Noise Energy

The stochastic resonance system has the advantage of making the noise energy transfer to the signal energy. Because the existing stochastic resonance system model has the problem of poor performance, an asymmetric piecewise linear stochastic resonance system model is proposed, and the parameters of the model are optimized by a genetic algorithm. The signal-to-noise ratio formula of the model is derived and analyzed, and the theoretical basis for better performance of the model is given. The influence of the asymmetric coefficient on system performance is studied, which provides guidance for the selection of initial optimization range when a genetic algorithm is used. At the same time, the formula is verified and analyzed by numerical simulation, and the correctness of the formula is proved. Finally, the model is applied to bearing fault detection, and an adaptive genetic algorithm is used to optimize the parameters of the system. The results show that the model has an excellent detection effect, which proves that the model has great potential in fault detection.

An improved adaptive stochastic resonance with general scale transformation to extract high-frequency characteristics in strong noise

2018 ◽  
Vol 32 (15) ◽  
pp. 1850185 ◽  
Author(s):  
Dawen Huang ◽  
Jianhua Yang ◽  
Jingling Zhang ◽  
Houguang Liu
Keyword(s):  
Stochastic Resonance ◽  
High Frequency ◽  
Signal To Noise Ratio ◽  
Scale Transformation ◽  
Weak Signal ◽  
Signal Features ◽  
Signal Characteristics ◽  
Periodic Signals ◽  
Sr Method ◽  
General Scale

The idea of general scale transformation is introduced in detail. Based on this idea, an improved adaptive stochastic resonance (SR) method is proposed to extract weak signal features. Different periodic signals are considered to verify the proposed method. Compared with the normalized scale transformation, the output signal-to-noise ratio (SNR) of the proposed method is increased to a greater extent. Further, the influences of some key parameters on the responses of the two methods are discussed minutely. Results show that the improved adaptive SR method with general scale transformation is obviously superior to the traditional normalized scale transformation that is used in the former literatures. For different noise intensities and time scales, the proposed approach can always obtain the optimal response of SR to enhance the weak signal characteristics.

scholarly journals Stochastic Resonance in a Multistable System Driven by Gaussian Noise

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Peiming Shi ◽  
Pei Li ◽  
Shujun An ◽  
Dongying Han
Keyword(s):  
Stochastic Resonance ◽  
Gaussian Noise ◽  
Noise Intensity ◽  
Signal To Noise Ratio ◽  
Gaussian White Noise ◽  
State Theory ◽  
Elimination Theory ◽  
Resonance System ◽  
System Parameters ◽  
Nonmonotonic Function

Stochastic resonance (SR) is investigated in a multistable system driven by Gaussian white noise. Using adiabatic elimination theory and three-state theory, the signal-to-noise ratio (SNR) is derived. We find the effects of the noise intensity and the resonance system parametersb,c, anddon the SNR; the results show that SNR is a nonmonotonic function of the noise intensity; therefore, a multistable SR is found in this system, and the value of the peak changes with changing the system parameters.

The Low Concentration Gas Detection Based on Genetic Algorithm Stochastic Resonance

2015 ◽  
Vol 738-739 ◽  
pp. 413-416
Author(s):  
Ji Jun Tong ◽  
Yan Qin Kang
Keyword(s):  
Genetic Algorithm ◽  
Stochastic Resonance ◽  
Signal To Noise Ratio ◽  
Optimization Theory ◽  
Gas Detection ◽  
Maximum Output ◽  
Resonance System ◽  
Detection Ability ◽  
Low Concentrations ◽  
The Relationship

The stochastic resonance (SR) theory provides a new idea for the detection of weak signal submerged in the strong noise. Combined with the optimization theory, this paper puts forward a stochastic resonance system based on genetic algorithm and applied it in a low concentrations gas detection. Firstly we preprocessed the input signal to satisfy the requirements of SR system, then developed the genetic algorithm to seek the maximum output signal-to-noise ratio (SNR), which was used to evaluate the performance of the system. In the end the relationship between the maximum SNR and concentration of gas was analyzed. The results of the experiments indicated the proposed method could improve the detection ability and enhance the detection limit of low gas concentrations.

An improved adaptive stochastic resonance method for improving the efficiency of bearing faults diagnosis

2017 ◽  
Vol 232 (13) ◽  
pp. 2352-2368 ◽  
Author(s):  
Dawen Huang ◽  
Jianhua Yang ◽  
Jingling Zhang ◽  
Houguang Liu
Keyword(s):  
Stochastic Resonance ◽  
High Frequency ◽  
Signal To Noise Ratio ◽  
Fault Simulation ◽  
Resonance Method ◽  
Bistable System ◽  
Scale Transformation ◽  
High Frequency Signal ◽  
Bearing Fault ◽  
Feature Information

The general scale transformation (GST) method is used in the bistable system to deal with the weak high-frequency signal submerged into the strong noisy background. Then, an adaptive stochastic resonance (ASR) method with the GST is put forward and realized by the quantum particle swarm optimization (QPSO) algorithm. Through the bearing fault simulation signal, the ASR method with the GST is compared with the normalized scale transformation (NST) stochastic resonance (SR). The results show that the efficiency of the GST method is higher than the NST in recognizing bearing fault feature information. In order to simulate the actual engineering environment, both the adaptive GST and the NST methods are implemented to deal with the same experimental signal, respectively. The signal-to-noise ratio (SNR) of the output is obviously improved by the GST method. Specifically, the efficiency is improved greatly to extract the weak high-frequency bearing fault feature information. Moreover, under different noise intensities, although the SNR is decreased versus the increase of the noise intensity, the ASR method with the GST is still better than the traditional NST SR. The proposed GST method and the related results might have referenced value in the problem of weak high-frequency feature extraction in engineering fields.

scholarly journals Comparative Study of Image Reconstruction Based on Compressive Sensing

2018 ◽  
pp. 243-247
Author(s):  
Himani A. Shah ◽  
Mr. Dipak Agrawal ◽  
Mr. Nimit Modi ◽  
Dr. Sheshang Degadwala
Keyword(s):  
Image Reconstruction ◽  
Compressive Sensing ◽  
High Frequency ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Sparse Signal ◽  
Large Problem ◽  
Matrix Measure ◽  
Performance Guarantees ◽  
Frequency Components

Compressive sensing based image reconstruction that improves the algorithm to applying different approach which is DWT and DCT. First, by using wavelet transform, wavelet low frequency of the sub bands in which the image is decomposed in to low frequency and high frequency wavelet coefficients, second is to applied DCT on low frequency coordinates and construct the different transformation matrix. Use the measurement matrix measure the high frequency coefficient components and combine with DCT low frequency components image and sparse signal output is applied on compressive sensing. In compressive sensing, random measurement matrices are generally used and ?1minimisation algorithms often use linear programming to cover sparse signal vectors. But explicitly constructible measurement matrices providing performance guarantees were and ?1minimisation algorithms are often demanding in computational complexity for applications involving very large problem dimensions. To improve the PSNR (pick signal to noise ratio) of reconstructions image uses different coding such as Huffman and Arithmetic.

Satellite Image Enhancement Using Dual Tree M-Band Wavelet Transform

2017 ◽  
Vol 8 (3) ◽  
pp. 737
Author(s):  
C. Periyasamy
Keyword(s):  
Wavelet Transform ◽  
High Frequency ◽  
Signal To Noise Ratio ◽  
Satellite Image ◽  
Resolution Enhancement ◽  
Intermediate Stage ◽  
Image Resolution ◽  
Enhancement Technique ◽  
Frequency Components ◽  
Visual Results

<p>Drawback of losing high frequency components suffers the resolution enhancement.  In this project, wavelet domain based image resolution enhancement technique using Dual Tree M-Band Wavelet Transform (DTMBWT) is proposed for resolution enhancement of the satellite images. Input images are decomposed by using DTMBWT in this proposed enhancement technique. Inverse DTMBWT is used to generate a new resolution enhanced image from the interpolation of high-frequency sub band images and the input low-resolution image. Intermediate stage has been proposed for estimating the high frequency sub bands to achieve a sharper image. It has been tested on benchmark images from public database. Peak Signal-To-Noise Ratio (PSNR) and visual results show the dominance of the proposed technique over the predictable and state-of-art image resolution enhancement techniques.</p>

scholarly journals Asymmetric second-order stochastic resonance weak fault feature extraction method

2020 ◽  
Vol 53 (5-6) ◽  
pp. 788-795
Author(s):  
Jiachen Tang ◽  
Boqiang Shi
Keyword(s):  
Stochastic Resonance ◽  
Output Signal ◽  
Background Noise ◽  
Signal To Noise Ratio ◽  
Resonance Method ◽  
Second Order ◽  
Resonance System ◽  
Signal To Noise ◽  
Weak Fault ◽  
Noise Ratio

To solve the problem that the weak fault signal is difficult to extract under strong background noise, an asymmetric second-order stochastic resonance method is proposed. By adjusting the damping factor and the asymmetry, weak signals, noise, and potential wells are matched to each other to achieve the best stochastic resonance state so that weak fault characteristics can be effectively extracted in strong background noise. Under adiabatic approximation, the effects of damping coefficient, noise intensity, and asymmetry on the output signal-to-noise ratio are discussed based on the two-state model theory. Under the same parameters, the output signal-to-noise ratio of the asymmetric second-order stochastic resonance system is better than that of the underdamped second-order stochastic resonance system. The bearing fault and field engineering experimental results are provided to justify the comparative advantage of the proposed method over the underdamped second-order stochastic resonance method.

Vibrational and Stochastic Resonance in FitzHugh-Nagumo Neural Model with Time-Delay and Colored Additive Noise

2011 ◽  
Vol 117-119 ◽  
pp. 685-689 ◽  
Author(s):  
Yu Rong Zhou ◽  
Zheng You He
Keyword(s):  
Stochastic Resonance ◽  
High Frequency ◽  
Adiabatic Approximation ◽  
Additive Noise ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Neural Model ◽  
Vibrational Resonance ◽  
Model Driven ◽  
Delayed Time

The vibrational resonance (VR) and stochastic resonance (SR) phenomena in time-delayed FitzHugh-Nagumo (FHN) neural model, driven by one high-frequency (HF) signal and one low-frequency (LF) signal, with coupled multiplicative and colored additive noise, is investigated. For the case that the frequency of the HF signal is much higher than that of the LF signal, under the adiabatic approximation condition, the expression of the signal-to-noise ratio (SNR) with respect to the LF signal is obtained. It is shown that, the SNR is a non-monotonous function of the amplitude and frequency of the HF signal. In addition, the SNR varies non-monotonically with increasing the intensities of the multiplicative and additive noise, with increasing the delayed-time as well as increasing the system parameters of the FHN model. The influence of the correlation time of the colored additive noise and the coupling strength between the multiplicative and additive noise on the SNR is discussed.

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