Signal Stochastic Resonance Research Articles

Retracted: Analysis of Asymmetric Piecewise Linear Stochastic Resonance Signal Processing Model Based on Genetic Algorithm

Retracted: Analysis of Asymmetric Piecewise Linear Stochastic Resonance Signal Processing Model Based on Genetic Algorithm

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.

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

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.

Magnetic Anomaly Signal Detection Using Parallel Monostable Stochastic Resonance System

The nonlinear stochastic resonance (SR) system possesses the ability of taking advantage of noise to enhance the weak signal when the SR system, signal and noise reach to the matching relation. It provides an effective approach to detect the weak magnetic anomaly signal in low signal-to-noise ratio. However, in practical applications, the measured magnetic anomaly signal may be a peak signal, a trough signal, or a combination of the two due to the uncertainty of magnetic target orientation. Hence it is difficult to maintain a good detection performance with single SR system because the SR system output is directly influenced by signal waveforms. Aiming to this, a new strategy using the parallel monostable SR (PMSR) system is proposed, which can ensure the good detection performance regardless of a peak signal, a trough signal, or a combination of the two. Besides, we take the kurtosis index as the criterion and search the optimal system parameters in SR system. The simulation and experiment results indicate its availability, validity and that it can achieve a good detection performance in different waveforms. It can be expected to be widely used in the field of magnetic anomaly detection with PMSR system.

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

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.

Novel synthetic index-based adaptive stochastic resonance method and its application in bearing fault diagnosis

Stochastic resonance (SR), which is characterized by the fact that proper noise can be utilized to enhance weak periodic signals, has been widely applied in weak signal detection. SR is a nonlinear parameterized filter, and the output signal relies on the system parameters for the deterministic input signal. The most commonly used index for parameter tuning in the SR procedure is the signal-to-noise ratio (SNR). However, using the SNR index to evaluate the denoising effect of SR quantitatively is insufficient when the target signal frequency cannot be estimated accurately. To address this issue, six different indexes, namely, power spectral kurtosis of the SR output signal, correlation coefficient between the SR output and the original signal, peak SNR, structural similarity, root mean square error, and smoothness, are constructed in this study to measure the SR output quantitatively. These six quantitative indexes are fused into a new synthetic quantitative index (SQI) via a back propagation neural network to guide the adaptive parameter selection of the SR procedure. The index fusion procedure reduces the instability of each index and thus improves the robustness of parameter tuning. In addition, genetic algorithm is utilized to quickly select the optimal SR parameters. The efficiency of bearing fault diagnosis is thus further improved. The effectiveness and efficiency of the proposed SQI-based adaptive SR method for bearing fault diagnosis are verified through numerical and experiment analyses.

Rotating machine fault diagnosis through enhanced stochastic resonance by full-wave signal construction

This study proposes a full-wave signal construction (FSC) strategy for enhancing rotating machine fault diagnosis by exploiting stochastic resonance (SR). The FSC strategy is utilized to transform a half-wave signal (e.g., an envelope signal) into a full-wave one by conducting a Mirror–Cycle–Add (MCA) operation. The constructed full-wave signal evenly modulates the bistable potential and makes the potential tilt back and forth smoothly. This effect provides the equivalent transition probabilities of particle bounce between the two potential wells. A stable SR output signal with better periodicity, which is beneficial to periodic signal detection, can be obtained. In addition, the MCA operation can improve the input signal-to-noise ratio by enhancing the periodic component while attenuating the noise components. These two advantages make the proposed FSCSR method surpass the traditional SR method in fault signal processing. Performance evaluation is conducted by numerical analysis and experimental verification. The proposed MCA-based FSC strategy has the potential to be a universal signal pre-processing technique. Moreover, the proposed FSCSR method can be used in rotating machine fault diagnosis and other areas related to weak signal detection.

Vibration Analysis of Civil Aero-Engine Based on Stochastic Resonance Preprocessing

Stochastic resonance (SR) in the bistable system can reduce the noise level, reveal the waveform’s outline prominently, and improve the quality of the system output. Under the support of SR techniques, the extraction accuracy of the civil aero-engine rotor vibration information is lifted, especially when treating the signals contaminated by heavy noise. Results indicate that the SR signal preprocessing method via tuning the relative parameters is effective to reflecting the real vibration of the aero-engine rotor. It can also monitor the short-term failure phenomenon in the sampling data.

Adaptive bistable stochastic resonance and its application in mechanical fault feature extraction

Stochastic resonance (SR) is an important approach to detect weak vibration signals from heavy background noise. In order to increase the calculation speed and improve the weak feature detection performance, a new bistable model has been built. With this model, an adaptive and fast SR method based on dyadic wavelet transform and least square system parameters solving is proposed in this paper. By adding the second-order differential item into the traditional bistable model, noise utilization can be increased and the quality of SR output signal can be improved. The iteration algorithm for implementing the adaptive SR is given. Compared with the traditional adaptive SR method, this algorithm does not need to set up the searching range and searching step size of the system parameters, but only requires a few iterations. The proposed method, discrete wavelet transform and the traditional adaptive SR method are applied to analyzing simulated vibration signals and extracting the fault feature of a rotor system. The contrastive results verify the superiority of the proposed method, and it can be effectively applied to weak mechanical fault feature extraction.

The Adaptive Stochastic Resonance Signal Detection System Based on the Multi-Point Random Search Algorithm

A model of adaptive stochastic resonance system based on the multi-point random search algorithm is proposed, and a weak signal detection system of adaptive stochastic resonance is established on the LabVIEW development platform. In this detection system, the typical nonlinear bistable system is used as the core of signal processing, and the system output signal-to-noise ratio is chosen as the optimizing objective function. The system structure parameters can be adjusted adaptively by using the multi-point random search algorithm, on which the optimal state of stochastic resonance of the system can be remained and the frequency of the weak signal can be obtained. Influences of the system parameters and the Gaussian noise on the stochastic resonance can be detected by this system. Experimental results show that this detection system is efficient, and has potential applications.

Research on Stochastic Resonance Signal’s Recovery

Using stochastic resonance to detect weak periodic signals has been widely used in various fields of science, which attracts much attention of researchers due to its advantages of revealing recessive periodic laws. This paper utilized this method to seek the underlying rule of setting weather index, so we can find that how to obtain the accurate expression of original periodic law by further investigation. This paper deals with the noise-contained signal restoring on the basis of the established system coupling the inversion system and bistable system. The simulation shows that this signal recovery method inversion effect is better and the application range is wider.

Enhanced Detection of Bearing Faults Based on Signal Cepstrum Pre-whitening and Stochastic Resonance

The impulse generated by bearing damage is interfered by discrete frequencies and much noise.The interference brings troubles to bearing faults detection.A new enhanced detection method of bearing faults is proposed based on combination of pre-whitening technology using cepstrum editing procedure(CEP) and weak signal detection based on stochastic resonance(SR) theory.Signal pre-whitening could be of advantage in enhancing the impulsiveness of the bearing signals,and the eventual result is a white signal,which contains both noise and the components resulting from localized bearing defects.The vibration signal pre-whitened is enveloped without the knowledge of optimal frequency band of bearing.The bearing faults character components could be enhanced,while bring more noise interference.The input of SR model is envelope signal of bearing.By normalized scale transform,bearing characteristic frequencies are enhanced.The local spectrum kurtosis and local signal-to-noise ratio of bearing faults characteristic components are proposed as indicators for comparison.The detection results of normal and outer race fault planted bearing show that the method proposed is better than the envelope analysis based on optimum spectrum kurtosis and signal pre-whitening only.

Energy fluctuations in a biharmonically driven nonlinear system

We study the fluctuations of work done and dissipated heat of a Brownian particle in a symmetric double well system. The system is driven by two periodic input signals that rock the potential simultaneously. Confinement in one preferred well can be achieved by modulating the relative phase between the drives. We show that in the presence of pumping the stochastic resonance signal is enhanced when analysed in terms of the average work done on the system per cycle. This is in contrast with the case when pumping is achieved by applying an external static bias, which degrades resonance. We analyse the nature of work and heat fluctuations and show that the steady state fluctuation theorem holds in this system.

Application of Stochastic Resonance Signal Recovery

Stochastic resonance(SR) enhances the nonlinear system behavior with the assistance of noise,including the sensitivity and selectivity of the response to the exterior stimulus. The energy-transfer mechanism makes the weak information revealed in the output spectrum,while the time-waveform is distorted. The distortion analysis was made both from the particle's dynamics and signal processing. The factors causing the deviation in the output are presented and the function of the recovery system is proposed. By the investigation of the particle's motion track in the bistable system and the suggested recovery system,the influences of noise and system parameters on the recovery course were discussed. Moreover,the pulse distortion appearing the recovery waveform caused by the particle's transitions at the bistable potential' inflexions was explained. Due to different characteristics,cascaded-bistable SR or mono-stable SR was introduced to process different types of signals. The final recovery signal is just the suggested recovery system's response to the SR output. Meanwhile,the recovery system is optional,as parameter-tuned or parameter-fixed. Since the method requires no average processing,it is applicable to a single sample with limited length. The numerical simulations reveal that the SR recovery method can recover the waveform containing weak information submerged in noise effectively. The engineering application to the vibration analysis of metal cutting chose the combination of mono-stable SR and the parameter-fixed recovery system. Because the optimal SR state is not required strongly,the system parameters are tuned in a wider range than the traditional SR processing methods.

On an aperiodic stochastic resonance signal processor and its application in digital watermarking

In this paper, an aperiodic stochastic resonance (ASR) signal processor for communication systems based on a bistable dynamic mechanism is proposed for detecting base-band binary pulse amplitude modulation (PAM) signals in communication systems. All parameters in the processor can be adjusted when needed. The adjustment mechanism is explained from the perspective of the conventional noise-induced nonlinear signal processing. To demonstrate this processor's usability, a digital image-watermarking algorithm in the discrete cosine transform (DCT) domain is implemented. In this algorithm, the watermark and the DCT alternating current (ac) coefficients of the image are viewed as the input signal and the channel noise, respectively. In conventional watermarking systems, it is difficult to explain why the detection bit error ratio (BER) of a watermarking system suffering from certain attacks is lower than that of the system not suffering from any attack. In the new watermarking algorithm, this phenomenon is systematically analyzed. It is shown that the DCT ac coefficients of an image as well as the noise imposed by the attacks can cooperate within the bistable system to improve the performance of the watermark detection.

Internal signal stochastic resonance of a synthetic gene network

The dynamics behavior of a synthetic gene network controlled by random noise is investigated using a model proposed recently. The phenomena of noise induced oscillation (NIO) of the protein concentrations and internal signal stochastic resonance (SR) are studied by computer simulation. We also find that there exists an optimal noise intensity that can most favor the occurrence of effective oscillation (EO). Finally we discuss the potential constructive roles of SR on gene expression systems.

Community stress, demoralization, and body mass index: evidence for social signal transduction

Quantification of the relationship between community-level chronic stress from neighborhood conditions and individual morale has rarely been reported. In this work, pregnant women were recruited at the prenatal clinics of Harlem Hospital and Columbia Presbyterian Medical Center in the USA, and given an initial questionnaire that included all 27 questions of the Dohrenwend demoralization instrument, as well as questions about household economics and health. An index of chronic community stress (ICCS) was compiled for each of the health areas of the study zone by standardizing and weighting each stressor significantly associated with low birthweight rate and summing the standardized, weighted values. Health areas were divided into ICCS quintiles. The graph of the quintile weighted averages of the index vs. the quintile averages of the demoralization score was an asymmetric inverted ‘U’ shape that fitted well to a stochastic resonance signal transduction model (adjusted R 2=0.73). On average, the women in the worst three quintiles were much heavier than those of the two best quintiles. Women reporting household economic deprivations were significantly more demoralized than the others. Median health area rents were strongly negatively associated with the ICCS. The worst average demoralization score occurred in the middle quintile, a state of coping with both poor community conditions and an economically strained household. Rents bridge community conditions and household economics.

Noise-enhanced energy transduction of molecular machinery

We have numerically investigated the enhance effect of noise on the energy transduction of molecular machinery. The energy transduction is enhanced with the increase of relative amplitude of the limit-cycle oscillation in polymer chain. And noise can enlarge the relative amplitude through a mechanism of stochastic resonance (SR). In the simulation, two different types of SR, internal signal SR (ISSR) and explicit ISSR (EISSR) are studied, respectively. And the similarity as well as difference between them was presented. The potential application of the enhance effect of energy transduction is then discussed.

Stochastic resonance effects in the scattering of neutrons by modulated two-state systems

Recent work indicates that the stochastic resonance phenomenon (SR) can have a strong signature in the neutron scattering cross section. Here we consider neutron scattering by a sample containing a random distribution of bistable scattering centres. The robustness of the stochastic resonance signal is tested by performing suitable configurational averages over the potential parameters. By showing that the resonant line is not significantly weakened by the randomness, our results suggest that SR should be observable in a real glass.