Stochastic Resonance Model Research Articles

Feature Extraction and Signal Enhancement Based on Unsaturation Piecewise Tri-Stable Stochastic Resonance Driven by Lévy Noise

As science advances and machines become larger and more sophisticated, it is vital to determine whether there is a mechanical failure without damaging the device. Stochastic resonance (SR), as a widely used method, can effectively extract the periodic signal in the noise and then realize the identification of mechanical faults, which is very important for safety and property protection. Most studies on SR are based on additive white Gaussian noise (AWGN) as the driving source; however, there are many kinds of noise in reality. In this paper, based on the previously proposed piecewise tri-stable SR (PTSR) model, the influence of parameters on the mean of signal-to-noise ratio gain (SNR-GM) of the system driven by Lévy noise is studied. It is also verified by simulation signals and actual signals that PTSR can also achieve feature extraction and signal enhancement with Lévy noise as the driving source, which proves that PTSR can be applied in a wider range of conditions.

Rolling bearing fault feature extraction method based on GWO-optimized adaptive stochastic resonance signal processing

The failure of rolling bearings affects the function and precision of rotating machinery significantly, which has drawn lots of attention in this field. Dealing with the failure of rolling bearings, fault feature extraction is the first and most important problem. In this work, we convert the bearing fault signal into stochastic resonance dynamics equivalently. And, adaptive stochastic resonance is adopted to extract the fault signal feature. In addition, for industrial application of fault signal processing with large amplitude and noise intensity greater than 1, normalized scale transformation is introduced into adaptive stochastic resonance and then solved by fifth-order Runge–Kutta algorithm. Then, to further optimize the solving precision of stochastic resonance model, the scaling coefficient and step size of Runge–Kutta algorithm are chosen with the help of Grey Wolf Optimizer (GWO). Thus, we can obtain a fast convergence speed, high calculation accuracy and effective improvement of signal-to-noise ratio fault feature extraction method for rolling bearing fault signal processing. Finally, a comparation simulation was carried out to demonstrate the efficiency of the proposed method. Compared with Cuckoo Search Optimizer-based stochastic resonance signal processing method, the proposed method achieved a higher signal-to-noise ratio (SNR) to benefit the fault feature extraction. In summary, this work gives out a more practical and effective solution for rolling bearing fault feature extraction in rotating machinery fault diagnosis field.

Gray Image Denoising Based on Array Stochastic Resonance and Improved Whale Optimization Algorithm

Aiming at the poor effect of traditional denoising algorithms on image enhancement with strong noise, an image denoising algorithm based on improved whale optimization algorithm and parameter adaptive array stochastic resonance is proposed in the paper. In this algorithm, through dimensionality reduction scanning, coding, modulation and other processing, the noise-containing gray image becomes a one-dimensional aperiodic binary pulse amplitude modulation signal suitable for a bistable stochastic resonance model. Then, the traditional whale optimization algorithm is improved in the initial solution distribution, global search ability and population diversity generalization. The improved whale optimization algorithm is applied to select the parameters of the stochastic resonance, which effectively improves the parameters self-adaptive of the array stochastic resonance model. Finally, the denoised image is obtained by demodulating, decoding and anti-scanning the stochastic resonance output. The experimental results show that compared with the array stochastic resonance method with fixed parameters and the classical image denoising method, the algorithm proposed in this paper has better performance in terms of visual effect and peak signal-to-noise ratio index, which proves the advantages and effective application of the method in image denoising.

Thermally driven single-electron stochastic resonance

Stochastic resonance (SR) in a single-electron system is expected to allow information to be correctly carried and processed by single electrons in the presence of thermal fluctuations. Here, we comprehensively study thermally driven single-electron SR. The response of the system to a weak voltage signal is formulated by considering the single-electron tunneling rate, instead of the Kramers’ rate generally used in conventional SR models. The model indicates that the response of the system is maximized at finite temperature and that the peak position is determined by the charging energy. This model quantitatively reproduces the results of a single-electron device simulator. Single-electron SR is also demonstrated using a GaAs-based single-electron system that integrates a quantum dot and a high-sensitivity charge detector. The developed model will contribute to our understanding of single-electron SR and will facilitate accurate prediction, design, and control of single-electron systems.

A new adaptive parallel resonance system based on cascaded feedback model of vibrational resonance and stochastic resonance and its application in fault detection of rolling bearings

Accurate extraction of weak feature information in strong background noise is a key to detect and identify rolling bearing faults. Stochastic resonance (SR) and vibrational resonance (VR) have received extensive attention and research in weak signal detection by reason of their advantages of utilizing additional inputs (i.e. noise or high frequency harmonic signals) to enhance weak signals. Considering the advantages and disadvantages of SR and VR in weak signal detection, this paper combines the two to construct a cascaded feedback model of VR and SR, and utilize it to form a parallel resonance system, which improves the detection performance of weak signals through the ensemble average effect. Furthermore, a multi-parameter optimization strategy based on the improved whale optimization algorithm (WOA) is proposed for the parameter selection of the parallel resonance system. It uses the constructed measurement index independent of the prior knowledge as the fitness function to realize automatic adjustment of multi-parameter, and obtains the final output by weighted summation of the optimal results obtained by multiple iterations. Finally, the suggested method is analyzed by numerical simulation signal and experimental data of rolling bearings, and the effectiveness and superiority of the proposed method in the detection of weak fault features are verified.

Rapid quality determination of cherry fruit (Prunus spp.) using artificial olfactory technique as combined with non-linear data extraction model

ABSTRACT In this article, quality rapid determination of cherry fruit (Prunus spp.) using artificial olfactory technique (AOT) combined with non-linear data extraction model was studied. AOT system was developed and used for cherry quality detection. AOT system responses to cherry samples stored at 4°C were recorded. At the same time, physical/chemical indexes, such as human sensory evaluation (HSE), firmness, color, pH, total soluble solids (TSS), and reducing sugar content (RSC), were examined to provide quality references to the cherry samples. AOT data was analyzed by principal component analysis (PCA), and bilayer stochastic resonance (BSR) models. PCA only partially discriminated the cherry samples. The signal-to-noise ratio (SNR) maximum values (SNR-Max) generated by BSR successfully discriminated all the samples. Multiple variable regression (MVR) between cherry physical/chemical indexes and BSR SNR-Max values was conducted. Results indicated that BSR was suitable for cherry quality rapid evaluation. Cherry quality examination model was built based on linear fitting regression on BSR eigen values. Validation tests results indicated that the developed model has good forecasting accuracy. The proposed method had some advantages, such as rapid responses, high accuracy, easy operation, etc.

Bearing Fault Diagnosis Based on Stochastic Resonance and Improved Whale Optimization Algorithm

In light of the problem of difficult model parameter selection and poor resonance effects in traditional bearing fault detection, this paper proposes a parameter-adaptive stochastic resonance algorithm based on an improved whale optimization algorithm (IWOA), which can effectively detect bearing fault signals of rotating machinery. First, the traditional WOA was improved with respect to initial solution distribution, global search ability and population diversity generalization, effectively improving the global convergence of the WOA. Then, the parameters of the bistable stochastic resonance model were optimized using the improved WOA, and adaptive adjustment of the stochastic resonance parameters was realized. Finally, the Case Western Reserve University bearing data set and the XJTU-SY bearing data set were used as fault data for the actual bearing to be tested for experimental verification. The signal-to-noise ratios of the detected fault frequencies for the above two data sets were −20.5727 and −21.1289, respectively. Among the algorithms compared, the IWOA proposed in this paper had the highest signal-to-noise ratio of the detected fault frequencies. The experimental results show that the proposed method can effectively detect a weak bearing fault signal in enhanced noise.

A Novel E-Exponential Stochastic Resonance Model and Weak Signal Detection Method for Steel Wire Rope

Steel wire rope can be frequently damaged by various factors. Although weak defect signal detection is the key to guarantee safety, current classic stochastic resonance (SR) models and methods are unavailable due to the complicated interference of noise. Thus, in this article, a novel E-exponential SR model and weak signal detection method are proposed. The principle of this model is first introduced from the perspective of Taylor expansion and nonlinear system analysis. Then, the model performance is simulated and evaluated by output signal analysis and chaos characterizations. Afterward, model comparison with different system parameters, experiments, and case studies regarding three typical wire rope defects inspection and weak signal recognition is conducted to demonstrate the validity of the novel method. Finally, the advantages and limitations of the proposed SR model are discussed.

RETRACTED: Risk analysis of a flywheel battery gearbox based on optimized stochastic resonance model

Flywheel battery is an important device for energy storage in a hybrid energy system. The gearbox not only affects the efficiency of the flywheel battery, but also the quality of the output electrical energy. Carrying out effective and feasible research on fault diagnosis of the flywheel battery gearbox can improve operational reliability and reduce operation and maintenance costs. Flywheel battery gearbox is often under variable speed working conditions, which makes it difficult to identify potential risk. In order to solve this problem, this study proposes an improved method of combining stochastic resonance method with calculation order analysis. This method aims to build an optimized bistable stochastic resonance model by adding second-order differential terms and wavelet analysis techniques to post-processing in the traditional stochastic resonance model. The results of simulation and experimental verification show that compared with the traditional stochastic resonance method, the proposed method has better noise utilization, significantly enhanced order amplitude and better recognition effect. Taking into account order amplitude, using optimized stochastic resonance increased it by 1.778.

Adaptive Enhancement for Coal-Rock Cutting Sound Based on Parameter Self-Tuning Bistable Stochastic Resonance Model

The traditional bistable stochastic resonance model has always had the drawback of being difficult when choosing accurate system parameters when a weak signal is enhanced. This paper proposes a parameter self-tuning adaptive optimization method based on the bat optimization algorithm to address this issue. The cubic mapping strategy of chaos optimization is introduced in the initial process of the individual position of the bat algorithm. Chaos is characterized by randomness, sensitivity, fractal dimension, and universality. The initial problem of the algorithm falling into local extremums is overcome. The global search capability of the basic bat optimization algorithm has been improved. The improved bat optimization algorithm’s objective function is the signal-to-noise ratio (SNR) of the target weak signal output by the bistable stochastic resonance model. An adaptive signal enhancement algorithm based on the improved bat optimization algorithm and bistable stochastic resonance (IBA-BSR) model is constructed to increase the proportion of weak signals in the mixed signal. Simulation signals are created to validate the proposed algorithm’s feasibility. The engineering application effect of this algorithm is further demonstrated by enhancing the sound signal of coal and rock cutting by a shearer in a coal face. Engineering test results demonstrate that this algorithm can significantly increase the SNR of coal and rock cutting sound signals by 42.4537 dB, and the effect is remarkable.

A New Piecewise Nonlinear Asymmetry Bistable Stochastic Resonance Model for Weak Fault Extraction

In order to solve output saturation problems found in traditional stochastic resonance methods and to improve the diagnosis ability of weak faults, a new piecewise nonlinear asymmetric bistable stochastic resonance (PNABSR) method is proposed. This model uses a left and right potential function with an asymmetrical shape, which makes it easier to induce stochastic resonance phenomena. Based on the PNABSR model, the expression of the signal-to-noise ratio (SNR) is derived, and the changes in the SNR with different parameters in the PNABSR model are analyzed. Then, the parameters in the PNABSR model are optimized using the adaptive intelligent algorithm to enhance the diagnostic ability. The diagnosis properties of the weak fault are compared between the PNABSR model and the classical bistable stochastic resonance model (CBSR). The experimental results prove that the PNABSR model can effectively extract the weak fault characteristic frequency under a strong noise background, verifying the effectiveness of this method.

Spatial–Temporal Stochastic Resonance Model for Dim-Small Target Detection

Stochastic resonance (SR) is usually utilized to enhance the signal with the help of noise. Inspired by this, we find the SR can also handle the problem of dim-small target detection under the low local signal-to-noise ratio (LSNR) situation. In this letter, we propose a novel spatial-temporal stochastic resonance (STSR) model for dim-small target detection. First, we select the SR as the core model to enhance the salience of the target by the inherent strong noise. With the help of the Poisson distribution prior, we employ the multiple adjacent frames as the input of the SR model, improving the LSNR of the resonance state through the temporal accumulation of photons. Then, we introduce the total variation regularization in the variational framework to remove the false alarm points by spatial smoothing, while preserving the role of noise in the SR. Finally, we customize an optimization process based on the alternating direction method of multiplier (ADMM) to solve the STSR variational minimization problem. Both the qualitative and quantitative experiments on real visible and infrared image sequences have demonstrated the superiority of the proposed model, especially in the low LSNR situation below 2 dB.

A high dimensional stochastic resonance system and its application in signal processing

At present, the stochastic resonance (SR) theory based on the traditional model with periodic input signal has been extensively studied. To further investigate dynamic process and improve the universality of SR system, a high-dimensional SR model is proposed in this paper. By analyzing the dynamic characteristics, the SR phenomenon is explained from the perspective of equilibrium point which is driven by the input signal. The expression of some key parameters is derived, and the noise reduction of aperiodic image signal is studied. Experimental results show that the SR phenomenon can be induced by appropriate input signal, and the proposed high-dimensional SR system has obvious noise reduction effect on aperiodic image signals, which further widens the application of SR theory in signal processing.

Simulated transient hearing loss improves auditory sensitivity

Recently, it was proposed that a processing principle called adaptive stochastic resonance plays a major role in the auditory system, and serves to maintain optimal sensitivity even to highly variable sound pressure levels. As a side effect, in case of reduced auditory input, such as permanent hearing loss or frequency specific deprivation, this mechanism may eventually lead to the perception of phantom sounds like tinnitus or the Zwicker tone illusion. Using computational modeling, the biological plausibility of this processing principle was already demonstrated. Here, we provide experimental results that further support the stochastic resonance model of auditory perception. In particular, Mongolian gerbils were exposed to moderate intensity, non-damaging long-term notched noise, which mimics hearing loss for frequencies within the notch. Remarkably, the animals developed significantly increased sensitivity, i.e. improved hearing thresholds, for the frequency centered within the notch, but not for frequencies outside the notch. In addition, most animals treated with the new paradigm showed identical behavioral signs of phantom sound perception (tinnitus) as animals with acoustic trauma induced tinnitus. In contrast, animals treated with broadband noise as a control condition did not show any significant threshold change, nor behavioral signs of phantom sound perception.

Rotating machinery weak fault signal detection method based on QPSO and stochastic resonance

Due to the weak early vibration signal of rotating machinery, which is nonlinear and non-stationary, the traditional filtering method has some limitations. To this end, a weak signal detection method combining Quantum Particle Swarm Optimization (QPSO) and Stochastic Resonance (SR) is proposed. First, according to the adiabatic approximation theory, make the original signal meet the SR requirements, and optimize the structural parameters of the SR through QPSO; secondly, the signal-to-noise ratio is used as the objective function to make the input signal and the output signal achieve the best match; finally, simulation and experimental data are used to verify the feasibility of the proposed method. Comparative analysis shows that the proposed method increases the amplitude by 46% compared with the characteristic frequency obtained by the PSO optimized SR model.

Research on the Cooperative Detection of Stochastic Resonance and Chaos for Weak SNR Signals in Measurement While Drilling.

In the process of drilling, severe downhole vibration causes attitude measurement sensors to be erroneous; the errors will accumulate gradually during the inclination calculation. As a result, the ultimate well path could deviate away from the planned trajectory. In order to solve this problem, this paper utilized the stochastic resonance (SR) and chaos phase transition (CPT) produced by the second-order Duffing system to identify the frequency and estimate the parameters of the signal during measurement while drilling. Firstly, the idea of a variable-scale is introduced in order to reconstruct the frequency of the attitude measurement signal, and an SR frequency detection model based on a scale transformation Duffing system is established in order to meet the frequency limit condition of the SR. Then, an attitude measurement signal with a known frequency value is input into the Duffing chaos system, and the scale transformation is used again to make the frequency value meet the parameter requirement of chaos detection. Finally, two Duffing oscillators with different initial phases of their driving signal are combined in order to estimate the amplitude and phase parameters of the measurement signal by using their CPT characteristics. The results of the laboratory test and the field-drilling data demonstrated that the proposed algorithm has good immunity to the interference noise in the attitude measurement sensor, improving the solution accuracy of the inclination in a severe noise environment and thus ensuring the dynamic stability of the well trajectory.

On the Breaking of the Milankovitch Cycles Triggered by Temperature Increase: The Stochastic Resonance Response

Recent decades have registered the hottest temperature variation in instrumentally recorded data history. The registered temperature rise is particularly significant in the so-called hot spot or sentinel regions, characterized by higher temperature increases in respect to the planet average value and by more marked connected effects. In this framework, in the present work, following the climate stochastic resonance model, the effects, due to a temperature increase independently from a specific trend, connected to the 105 year Milankovitch cycle were tested. As a result, a breaking scenario induced by global warming is forecasted. More specifically, a wavelet analysis, innovatively performed with different sampling times, allowed us, besides to fully characterize the cycles periodicities, to quantitatively determine the stochastic resonance conditions by optimizing the noise level. Starting from these system resonance conditions, numerical simulations for increasing planet temperatures have been performed. The obtained results show that an increase of the Earth temperature boosts a transition towards a chaotic regime where the Milankovitch cycle effects disappear. These results put into evidence the so-called threshold effect, namely the fact that also a small temperature increase can give rise to great effects above a given threshold, furnish a perspective point of view of a possible future climate scenario, and provide an account of the ongoing registered intensity increase of extreme meteorological events.

Stochastic resonance and 'gamma band' synchronization in the human visual system.

Cortical synchronization in the gamma-frequency range (above ~30.0 Hz) and the signal/noise interplay described by stochastic resonance models have been proposed as basic mechanisms in neuronal synchronization and sensory information processing, particularly in vision. Here we report an observation in humans of linear and inverted-U distributions of the electrophysiological (EEG) responses to visual contrast stimulation in the gamma band and in the low frequency components of the visual evoked responses (VER), respectively. The combination of linear and inverted-U distributions is described by a stochastic resonance model (SR). The observation needs replication in larger subjects’ samples. It nevertheless adds to the available evidence of a role of gamma oscillatory signals and SR mechanisms in neuronal synchronization and visual processing. Some functional adaptation in human vision appears conceivable and further investigation is warranted.

A novel stochastic resonance model based on bistable stochastic pooling network and its application

• A novel stochastic resonance model based on bistable stochastic pooling network is proposed. • The least mean square algorithm is used to optimize the BSPN output vector optimized by random noise with linear weighting. • The SNR of the output signal is deduced, and the change of SNR with the number of network nodes is discussed. • Some examples are used to verify the weak signal detection capability of the BSPN model. Analysing the vibration and sound signals of machine components is the primary approach for machine condition monitoring and fault diagnosis. However, due to the special working operating conditions of rotating machinery, the collected signals often contain strong noise components generated by other parts of the machine and harsh environment. These noises severely affect the analysis and processing of the target signal. Stochastic resonance (SR) is an effective technique to extract and enhance periodic or aperiodic signals submerged in noise. Consequently, SR has been widely used for fault diagnosis of rotating machinery. In this study, a bistable stochastic pooling network (BSPN) model based on the traditional SR model is proposed to improve the efficiency of weak fault diagnosis. The least mean square algorithm is used to perform linear weighted optimization on the output vector of random noise-optimized BSPN. At the same time, the optimal weight vector of the random stochastic pooling networks with any number of nodes is obtained. Subsequently, analog signals are used to examine the output signal-to-noise ratio (SNR) of the BSPN. Finally, the efficacy of BSPN system is validated through bearing data collected by two different experimental systems. The experimental results indicate that ordinary array system cannot avoid frequency conversion interference, so it is unable to extract extremely weak fault signals. On the contrary, the BSPN system can accurately detect the weak.

Stochastic Resonance in Thermally Bistable Josephson Weak Links and Micro-SQUIDs

Constriction-based Josephson weak-links display a thermal bi-stability between two states exhibiting zero and finite voltages. This manifests in experiments either as hysteresis in weak-links current voltage characteristics or as random telegraphic signal in voltage. In the latter case, a noise-driven amplification of a sinusoidal excitation of the device is observed, at frequencies matching the characteristic switching frequency in telegraphic signal, a phenomenon known as stochastic resonance. The observed behavior is understood using a two-state model of stochastic resonance and is exploited to illustrate an enhanced signal-to-noise-ratio in a micro-SQUID as a magnetic field sensor.