Adaptive Stochastic Resonance Research Articles

Adaptive stochastic resonance under Poisson white noise background and its application for bolt looseness detection

Adaptive stochastic resonance under Poisson white noise background and its application for bolt looseness detection

A feature extraction method of rub-impact based on adaptive stochastic resonance and Hjorth parameter.

The characteristic frequency of a rub-impact fault is usually very complex and may contain higher harmonics and subharmonics. Due to the uncertainty of harmonic components and the complexity of signal-to-noise ratio (SNR) operation, the general scale transformation stochastic resonance (GSTSR) has certain limitations in the identification of rub-impact faults. To solve this problem, the paper starts with complexity and proposes a rub-impact fault identification method combining a swarm intelligence optimized algorithm (SIOA) with Hjorth parameters and GSTSR. The complexity of vibration signals will change greatly before and after rub-impact faults. The complexity parameter in Hjorth parameters can effectively embody the complexity of signals and is invulnerable to noise interference. Therefore, the complexity parameter in the Hjorth parameters is taken as the objective function of SIOA and combined with GSTSR. Vibration signals from cases are taken as input to adaptive stochastic resonant (ASR) systems, and the system parameters are adaptively and synchronously adjusted to realize the maximal resonant effect. Finally, the spectrum analysis of signals obtained from ASR is used to extract failure features and recognize faults in the rotor-stator rub-impact. The proposed method is verified by comparing it with other schemes under different SIOAs and different operating conditions. The result of the comparison shows that the complexity parameter of the Hjorth parameters can be taken as the objective function of SIOA to accurately identify the rub-impact fault. Meanwhile, the proposed method, compared with the method of taking SNR as an objective function, has a better effect on reducing time costs and strengthening fault characteristics.

Adaptive bistable stochastic resonance based weak signal reception in additive Laplacian noise

Adaptive bistable stochastic resonance based weak signal reception in additive Laplacian noise

Modulation level adaptive stochastic resonance receiver with low-resolution ADCs for multilevel amplitude modulated signals

Modulation level adaptive stochastic resonance receiver with low-resolution ADCs for multilevel amplitude modulated signals

Early Weak Fault Signal Enhancement and Recognition Method of Rudder Paddle Bearings Based on Parameter Adaptive Stochastic Resonance

Early Weak Fault Signal Enhancement and Recognition Method of Rudder Paddle Bearings Based on Parameter Adaptive Stochastic Resonance

Motor Bearing Fault Diagnosis in an Industrial Robot Under Complex Variable Speed Conditions

Abstract Motor bearing is the key vulnerable part of the servomotor in an industrial robot, which is always arranged at the joint that is the main load area. In the movement process of the robot, motor bearing bears a great impact due to the frequent movement of joints, which is easily damaged. The fault characteristic information of a bearing in these complex conditions shows strong nonstationary characteristics. Early nonstationary fault signals are often weak and submerged in background noise. The nonstationary signal processing method using computed order analysis and the weak signal enhancement method using adaptive stochastic resonance both show good performances for the above problems. Inspired by these, a hybrid diagnosis strategy for motor bearing under these speed conditions is proposed. Firstly, the nonstationary fault signals of the motor bearing are transformed into stationary angular signals via computed order analysis. Then, the fault modes are identified via resonance demodulation and variational mode decomposition in the order spectrum. Finally, adaptive stochastic resonance is used to extract the fault features reflecting the bearing operation state. Two types of typical speed conditions are considered, which are representative of the joint. Numerical simulation analysis and experiments verify the effectiveness of the diagnosis method.

4FSK signal processing based on adaptive tristable stochastic resonance under levy noise

Aiming at the problem of low quality of coherent demodulation transmission signal of digital signal in the background of Levy noise, this paper proposes a new method of 4FSK signal reception based on adaptive tristable stochastic resonance (SR) system. The bit error rate (BER) of output signals of adaptive tristable SR system model and the traditional modulation model are compared by simulation. The numerical simulation results show that compared with the traditional model, the output BER of the 4FSK signal after demodulation of the adaptive tristable SR system model is reduced by 4% compared with the traditional model. The time domain image of 4FSK signal is clearer, the burrs are significantly reduced and the spectral amplitude of 4FSK signal is greatly improved. The success of this experiment suggests a strong promise of SR systems for digital signal detection in impact noise.

Optimized injection of noise in activation functions to improve generalization of neural networks

This paper proposes a flexible probabilistic activation function that enhances the training and operation of artificial neural networks by intentionally injecting noise to gain additional control over the response of each neuron. During the learning phase, the level of injected noise is iteratively optimized by gradient-descent, realizing a form of adaptive stochastic resonance. From simple hard-threshold non-differentiable neuronal responses, controlled injection of noise gives access to a wide range of useful activation functions, with sufficient differentiability to enable gradient-descent learning for both the neuron and the injected-noise levels. Experimental results on function approximation demonstrate injected noise generally converging to non-vanishing optimal levels associated with improved generalization abilities in the neural networks. A theoretical explanation of the generalization improvement based on the path norm bound is presented. With injected noise in the deep neural network, experimental results on classifying images also obtain non-vanishing optimal noise levels to achieve better testing accuracies. The proposed probabilistic activation functions show the potential of adaptive stochastic resonance for useful applications in machine learning.

Dual-channel two-dimensional stochastic resonance and its application in bearing fault detection under alpha-stable noise

Coupled two-dimensional stochastic resonance (CTDSR) systems exhibit a higher quantity of steady-states and hold significant theoretical and practical value. However, the coupling between nonlinear systems generates multiplicative noise, which impairs signal transmission. In this paper, a novel dual-channel two-dimensional stochastic resonance (DCTDSR) model is proposed to harness multiplicative noise for enhancing system output. It is evident from the potential well analysis that multiplicative noise causes variations in the properties and amounts (from 2 to 4) of resonance peaks in the DCTDSR system. The steady-state probability density (SPD) of the novel system is derived by implementing the adiabatic approximation theory. In the background of alpha-stable noise, simulation results indicate that the DCTDSR system exhibits heightened detection capabilities for weak signals that are immersed in severe impulsive and heavy-tailed noise. Based on the stability of alpha-stable noise, an adaptive stochastic resonance fault diagnosis method that adjusts both parameters and multiplicative noise is designed to overcome the issue of insufficient single energy regulation. Experimental results have demonstrated that the DCTDSR system boosts noise energy transmission and offers improved bearing fault detection accuracy. Thus, effectively utilizing multiplicative alpha-stable noise can further enhance the practical applicability of stochastic resonance theory in engineering.

Adaptive detection of impact signals with two-dimensional piecewise tri-stable stochastic resonance and its application in bearing fault diagnosis

In this paper, we introduce an innovative method known as Adaptive Two-Dimensional Piecewise Tri-Stable Stochastic Resonance (TDPTSR) and discuss its applicability in extracting weak fault features. Initially, addressing the saturation issue of the Standard Tri-Stable Stochastic Resonance (STSR) system, we construct a novel piecewise tri-stable potential function. This function independently adjusts the depth and width of potential wells. We explore the particle output characteristics of the Piecewise Tri-Stable Stochastic Resonance (PTSR) system using the fourth-order Runge-Kutta algorithm. Subsequently, we delve into the performance analysis of the TDPTSR system. Leveraging adiabatic approximation theory, we derive the equivalent potential function, steady-state probability density (SPD), mean first-pass time (MFPT), and output signal-to-noise ratio (SNR) of the TDPTSR. We also investigate the impact of changes in system parameters on these metrics. Additionally, drawing from kurtosis and cosine similarity properties, we introduce a Modified Kurtosis Index (MKC) as a measurement index for impact signal detection. We propose a method for impact signal feature extraction by integrating MKC with the TDPTSR system. Simulation analysis demonstrates the suitability of MKC as an index for quantifying Stochastic Resonance and the superior detection capability of the TDPTSR system. Finally, we apply TDPTSR to the fault diagnosis of two types of bearings, optimizing system parameters using a genetic algorithm (GA). Comparative analysis with STSR and PTSR confirms that our proposed method in this paper yields further improvements in Signal-to-Noise Ratio Improvement (SNRI), higher spectral peak magnitude (Amout) at characteristic frequencies, and increased recognition quantity (Δ).

Adaptive bistable stochastic resonance based blind watermark extraction in discrete cosine transform domain

AbstractBlind watermark extraction in discrete cosine transform (DCT) domain has a wide application prospect as well as a challenging subject. The imperceptibility of watermark signal makes watermark extraction a weak signal reception issue in essence. For DCT coefficients of host image generally disobey Gaussian distribution, at which the performance of linear correlated reception is no longer optimal. Aiming at this, a novel blind watermark extraction scheme combining the uncorrelated reception with adaptive bistable stochastic resonance (ABSR) technique is proposed. First, by block DCT transformation for host image, an additive watermark embedding algorithm is introduced, in which the watermarked image can be converted to one dimensional time domain weak signal (binary watermark image) reception under additive Laplacian noise (selected DCT coefficients). On this basis, through the key technology research on quantitatively cooperative resonance relationship under Laplacian noise, the ABSR system can be implemented by bistable system parameters self‐adaptive adjustment, in which the ABSR system output signal will be enhanced rather than be weakened by random noise. Finally, the ABSR‐based watermark extraction scheme is investigated, and both the visual effect, bit error ratio performance and robustness of proposed scheme are testified to be superior to that of traditional uncorrelated extraction.

Image denoising using adaptive bi-dimensional stochastic resonance system

Using stochastic resonance (SR) mechanism, the output signal can be enhanced by adding noise to the nonlinear system. Therefore, an image denoising algorithm based on adaptive bi-dimensional stochastic resonance (ABSR) is proposed in this paper. Firstly, the image is sampled as a bi-dimensional signal, and an adaptive bi-dimensional dynamic nonlinear system model is constructed. The peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the output image are used as the double evaluation model of the adaptive system, and the optimal parameters of the model are automatically obtained by adjusting the parameters of the dynamic nonlinear system using the reverse positioning method. Compared with the traditional mean filter, median filter and one-dimensional stochastic resonance, the image restoration effect of dynamic adaptive bi-dimensional stochastic resonance is more closer to the original image, and the histogram, PSNR and SSIM of the output image are also significantly better than the other three methods. The results show that dynamic adaptive bi-dimensional stochastic resonance has better denoising effect and better robustness to the change of noise intensity in image processing.

A novel adaptive weak fault diagnosis method based on modulation periodic stochastic pooling networks

Stochastic resonance, known for its strong capability to amplify weak signals, has been widely applied in rotating machinery fault diagnosis. However, the increasing intelligence of mechanical equipment and the harsh service environment leads to new challenges for stochastic resonance method. Moreover, the adaptive stochastic resonance system relying on the signal-to-noise ratio (SNR) as the loss function requires extensive prior knowledge of the signal to be measured, limiting its application in engineering. Therefore, this paper presents a modulation periodic stochastic pooling networks (MPSPN) with integral modulation factor. By using the normalized least-mean-square (NLMS)algorithm, an adaptive bearing fault diagnosis method based on MPSPN under unknown faults is developed. The study first proposes a modulated periodic stochastic resonance (MPSR) model and investigates its stochastic resonance characteristics through the steady-state probability density. Then, it introduces a modulation signal detection index (IMBF) and derives an adaptive weight allocation scheme under NLMS optimization. Finally, the superiority of the MPSPN system is demonstrated through simulations and the analysis of bearing fault data obtained from two distinct experimental platforms. The results indicate that, in comparison to the conventional periodic stochastic resonance (PSR) system, the MPSPN system is capable of effectively diagnosing unknown faults in bearings and significantly improving the SNR of the diagnostic output.

An adaptive stochastic resonance detection method based on a fast artificial fish swarm algorithm

A new stochastic resonance method that is based on a fast artificial fish swarm algorithm has been proposed in an effort to address the adaptive parameter-induced stochastic resonance for weak signal detection’s slow convergence time. The target evaluation function for the system is the output signal-to-noise ratio. The method of scale transformation and amplitude compression is used to pre-process the high frequency and large parameter signals. To achieve fast adaptive detection that applies to weak communication signals, the stochastic resonance system’s characteristics are used to constrain the optimization iteration rules.According to the simulation results, the fast artificial fish swarm method has significantly better optimization efficiency and achieves the same optimization results as the basic artificial fish swarm algorithm while reducing convergence time by 74.89%.

Research on feature extraction method of spindle vibration detection of weak signals for rapier loom fault diagnosis in strong noise background

Fault diagnosis of rapier loom is an inevitable requirement to meet the demand of intelligent manufacturing. Facing the strong noise interference caused by complex working environment, accurate and reliable vibration signal detection of blade loom spindle is the key to realize the rapier loom fault diagnosis. This paper proposes a method to extract the spindle vibration signal of the rapier loom by Adaptive Piecewise Hybrid Stochastic Resonance (APHSR) after the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN). Firstly, ICEEMDAN is used to pre-process the weak vibration signal containing noise, decompose the signal into multiple IMF components and display the high and low frequency signal characteristics of the original signal. Then, the energy density method and the correlation coefficient method are used to remove high and low noise, respectively, to filter the optimal IMF components, and then the signal containing valid information is reconstructed. Finally, the reconstructed signal is input to APHSR for noise-assisted enhancement after scale transformation to restore the faint vibration signal feature frequencies and achieve effective feature extraction. Through the simulation experiment and the engineering fault experiment analysis, comparing ICEEMDAN-APHSR with CEEMDAN-SR, ICEEMDAN-SR, CEEMDAN-APHSR methods. The difference between the spectrum amplitude, the spectrum amplitude and the maximum noise and the maximum signal to noise ratio (SNR) of the fault feature frequency of the rapier loom spindle bearing increased by 3.3668 dB,1.7205 dB,2.3952 dB, respectively. The results show that ICEEMDAN-APHSR method can accurately extract the fault feature frequency of the spindle bearing of rapier loom, and effectively solves the problem of extracting the weak vibration signal feature of rapier loom in the background of strong noise. This method is beneficial to the future research of rapier loom fault diagnosis, and is of great significance to promote the maintenance of loom equipment and production safety and quality.

Optimal Noise-Boosted Estimator Design Via Adaptive Stochastic Resonance

In signal estimation, an optimal estimator is frequently unachievable because its closed form may not be analytically tractable or is too complex to implement. Alternatively, one can turn to suboptimal yet easily implementable estimators for practical signal estimation tasks. In this paper, an optimal noise-boosted estimator is designed and the adaptive stochastic resonance method is implemented to simultaneously exploit the beneficial role of the injected noise as well as the learning ability of the estimator parameter. Aiming to effectively improve the estimation performance, we use the kernel function method to find an approximate solution for the probability density function (PDF) of the optimal injected noise. During this process, the noise PDF and the estimator parameter establish a finite-dimensional non-convex optimization space for maximizing the estimation performance, which is adaptively searched by the sequential quadratic programming (SQP) algorithm at each iteration. Two representative estimation problems are explored. The obtained results demonstrate that this adaptive stochastic resonance method can improve the performance of the suboptimal estimators and bring it very close to that of the optimal estimator.

Weak signal enhancement for rolling bearing fault diagnosis based on adaptive optimized VMD and SR under strong noise background

Owing to the nonlinearity and nonstationarity of the bearing fault signal, it is difficult to identify fault characteristics under the influence of a strong noise environment. The extraction of early weak fault features is critical for the reliability of bearing operations. Therefore, an urgent problem is reasonable noise reduction and feature enhancement in weak-signal processing. Traditional variational modal decomposition (VMD) and stochastic resonance (SR) are commonly applied to detect weak signals in fault diagnosis. The VMD method can decompose the signal into several intrinsic mode functions (IMFs) to effectively reduce the modal aliasing problem. However, uniform standards for the key parameters of decomposition and the selection of the optimal IMF after decomposition are lacking. Meanwhile, some disadvantages of SR still exist; for example, the interference of multiscale noise may lead to false detection by incorrect selection of high-pass filter parameters, and the system parameters are not adaptive to different signals to achieve the best response output. To better address the weak signal feature enhancement, a novel rolling bearing fault diagnosis method combining adaptive VMD and SR by improved differential search (IDS) optimization is proposed. First, the bearing fault signal is decomposed into several IMFs using the IDS-VMD. Second, the feature information of the fault signal is retained and reconstructed using the correlation kurtosis for sensitive modal extraction. Furthermore, the fault features of the reconstructed signal are effectively enhanced by the variable-step IDS-SR, which can reasonably transfer the noise energy of the input components to the fault characteristic frequency. Finally, the periodic pulse can be observed in the corresponding envelope spectrum. The simulated and experimental data show that the proposed method can not only effectively extract the signal feature information in the actual fault but also realize early weak fault diagnosis of rolling bearings more accurately.

A novel image noise reduction method for composite multistable stochastic resonance systems

In the field of digital signal processing, image denoising is an more and more significant research direction. For the traditional noise reduction theory, noise is considered to be harmful, and the image quality can be improved by analyzing noise characteristics and filtering noise. The appearance of stochastic resonance theory proves that noise can be used to enhance signal, which brings new inspiration to image processing. The classical bistable stochastic resonance model has the problems of high potential barrier and easy saturation, which is not conducive to the improvement of image denoising effect. In this paper, a novel type of stochastic resonance potential well model is quoted, which solves the above shortcomings of the bistable stochastic resonance model, and then combines it with the Gaussian model to propose a composite multistable stochastic resonance model. The dynamic principle of the model in signal detection is described, and the influence of system parameters on image noise reduction is analyzed. The whale optimization algorithm is used to optimize the model parameters, and an adaptive compound multistable stochastic resonance system is established to process pictures and measured radar images under different noise backgrounds. The simulation experiment and engineering application show that the model proposed in this paper solves the problem of high potential barrier and easy saturation of the bistable model, and has better image noise reduction ability compared with Wiener filter, median filter, classical bistable stochastic resonance system and novel type of stochastic resonance potential well system.

Adaptive progressive learning stochastic resonance for weak signal detection

Stochastic resonance (SR) can enhance signals by using noise. This has attracted more attention in the field of weak signal detection. In practical applications, owing to the non-adjustability of noisy signals, SR is required to adjust the system parameters adaptively to satisfy the conditions of the SR phenomenon. In this paper, an adaptive progressive learning SR method is proposed to improve the detection ability for weak signal, and the SR phenomenon is quantitatively defined. A theoretical learning framework is established with an improved reinforcement learning model by mapping the nonlinear system parameter space to a progressive learning set. By selecting a proper learning layer within a determined constraint range, the matching system parameters can be quickly and accurately searched to generate a desired optimal output. Numerical simulation results show that the signal energy and the output signal-to-noise ratio (SNR) can be enhanced significantly, which reflects an excellent weak signal detection performance especially for low SNR conditions. Finally, a diagnosis of the outer race fault signals of a rolling bearing confirms that the proposed method can effectively detect fault characteristics.

Nonstationary feature extraction based on stochastic resonance and its application in rolling bearing fault diagnosis under strong noise background.

When the load and speed of rotating machinery change, the vibration signal of rolling bearing presents an obvious nonstationary characteristic. Stochastic resonance (SR) mainly is convenient to analyze the stationary feature of vibration signals with high signal-to-noise ratio. However, it is difficult for SR to extract the nonstationary feature of rolling bearings under strong noise background. For one thing, the frequency change of nonstationary signals makes the occurrence of SR very difficult. For another, the features of rolling bearings are large parameters and further prevent the SR method from performing well. Therefore, combined with order analysis (OA), adaptive frequency-shift SR is presented in this paper. To solve the problem of frequency change, OA is used to convert the nonstationary feature into stationary feature, which resamples the nonstationary signal in the time domain to stationary signal in the angular domain. To solve the other problem, the frequency-shift method based on Fourier transform is adopted to move the fault feature frequency to low frequency, and thus SR is more likely to occur under small parameter conditions. The simulated and experimental results indicate that not only the amplitude of fault feature but also the signal-to-noise ratio is significantly improved. These demonstrate that the fault features of rolling bearing in variable speed conditions are extracted successfully.