Stochastic Resonance System Parameters Research Articles

Adaptive stochastic resonance enhanced weak linear frequency modulated signal perception in low signal-to-noise ratio environments

Abstract The linear frequency modulated (LFM) signal has gained extensive utilization in radar, sonar and covert communication system due to its large time-bandwidth product, high-precision distance measurement and low detection probability. The perception of weak LMF signals holds significant importance yet encounters substantial challenges within the increasing complexity of the electromagnetic environments. Consequently, this paper proposes an adaptive stochastic resonance (ASR) enhanced approach for perceiving weak LFM signals in low signal-to-noise ratio (SNR) conditions. This approach initially commences a pioneering study on the quantitative synergistic resonance mechanism among LFM signals, random noise, and nonlinear stochastic resonance (SR) systems. Subsequently, the ASR system’s implementation becomes straightforward through the adaptive adjustment of SR system parameters according to the LFM signal and noise characteristics. This implementation leverages the inherent property of noise energy transfer to signal energy, facilitating the enhancement of ordered weak signals via random noise. Following the enhancement of the output signal by the ASR system, the Wigner–Ville distribution (WVD) transform’s time-frequency concentration property is employed to extract the time-frequency characteristics. Building on the WVD transform, the Radon transform with linear integral projection is applied to further harness the time-frequency domain energy concentration, thereby achieving the effective perception of weak LFM signals. Finally, the effectiveness of the proposed method in improving the perception performance of weak LFM signal in very low SNR conditions is verified through numerical simulations. It is anticipated that this method will have potential application value in fields such as radar, sonar, and communication under complex electromagnetic environments.

Hidden Markov Model based Stochastic Resonance and its Application to Bearing Fault Diagnosis

Rolling bearings are crucial components in rotating machinery and the detection of damage at early stages is pivotal to ensure the life of the machine. Thus, developing accurate methods to identify the presence of faults captured using vibration measurements early on are necessary. Stochastic resonance (SR) is an interesting technique for bearing fault diagnosis, which by adding proper amount of noise to the signal the faulty signal can be extracted. However, traditional SR methods require that the analyst already know the characteristic frequency associated with the fault to be able to extract the corresponding weak signal. This paper overcomes this shortage by adaptively searching the SR system parameters by using Box-Cox sparse measures (BCSM) – based continuous hidden Markov model (CHMM) to determine the characteristic frequency directly from measured data. The method consists of a training phase that uses a simulated outer race fault to determine the optimal CHMM parameters and an application phase where the CHMM is used to determine the characteristic frequency directly from the unprocessed measured signal. The proposed method is verified using three well-known real bearing data sets and is found to be successful in all applications considered here.

Incipient fault diagnosis of planetary gearboxes based on an adaptive parameter-induced stochastic resonance method

The stochastic resonance (SR) method is commonly used in incipient fault diagnosis to extract weak fault features from complex diagnostic signals. However, the extraction effect of a SR system is highly dependent on the choice of system parameters as well as the complexity of input signals. To solve this problem, the present study proposes an adaptive parameter-induced SR method, in which high pass filter and Teager energy operator (TEO) are combined to pre-process the original signal while the grasshopper optimization algorithm is introduced to optimize the SR system parameters. The proposed method is employed to diagnose a set of experimental vibration signals of a planetary gearbox with incipient localized root crack and incipient distributed surface wear as well, leading to satisfactory diagnosis results. Comparing with the existing adaptive stochastic resonance methods, the present method claims the merits of a high signal-to-noise ratio and low computation cost.

Rolling Bearing Fault Diagnosis Based on Adaptive Multiparameter-Adjusting Bistable Stochastic Resonance

The weak-signal detection technologies based on stochastic resonance (SR) play important roles in the vibration-based health monitoring and fault diagnosis of rolling bearings, especially at their early-fault stage. Aiming at the parameter-fixed vibration signals in practical engineering, it is feasible to diagnose the potential rolling bearing faults through adaptively adjusting the SR system parameters, as well as other generalized parameters such as the amplitude-transformation coefficient and scale-transformation coefficient. However, extant adaptive adjustment methods focus on the system parameters, while the adjustments of other adjustable parameters have not been fully studied, thus limiting the detection performance of the adaptive SR method. In order to further enhance the detection performance of adaptive SR methods and extend their application in rolling bearing fault diagnosis, an adaptive multiparameter-adjusting SR (AMPASR) method for bistable systems based on particle swarm optimization (PSO) algorithm is proposed in this paper. This method can produce optimal SR output through adaptively adjusting multiparameters, thus realizing fault feature extraction and further fault diagnosis. Furthermore, the influence of algorithm parameters on the optimization results is discussed, and the optimization results of the Langevin system and the Duffing system are compared. Finally, we propose a weak-signal detection method based on the AMPASR of the Duffing system and employ three diagnosis examples involving inner ring fault, outer ring fault, and rolling element fault diagnoses to demonstrate its feasibility in rolling bearing fault diagnosis.

A Novel Underwater Location Beacon Signal Detection Method Based on Mixing and Normalizing Stochastic Resonance.

A flight data recorder (FDR) is an electronic recording device placed in an aircraft for the purpose of facilitating the investigation of aviation accidents. If an aircraft crashes into water, an underwater locator beacon (ULB), which is installed on the FDR, is triggered by water immersion, and emits an ultrasonic 10 ms pulse signal once per second at 37.5 kHz. This pulse signal can be detected by sonar equipment. However, the ULB signal only can be detectable 1–2 kilometers from the surface in normal conditions. Stochastic resonance (SR) is a rising theory in the field of weak signal detection. The classical stochastic resonance limits state that the input must be small-parameter and the sampling frequency must be 50 times higher than the signal frequency. It cannot be applied to the ULB signal detection. To resolve this problem, this paper presents a novel approach named mixing and normalizing stochastic resonance (MNSR). By mixing the ULB signal and normalizing SR system parameters, MNSR provides a new way to detect weak ULB signal. Meanwhile, we propose the parameters adjustment method of MNSR. We prove the effectiveness through numerical simulation. An experiment in a tank is employed to verify the practicability of this method.

Adaptive parameter-tuning stochastic resonance based on SVD and its application in weak IF digital signal enhancement

Parameter-tuning stochastic resonance can effectively use noise to enhance signal energy, whereas its system parameters are hard to select, and how to combine it with more practical signals needs to be researched. In this study, the IF (intermediate frequency) digital signal with low SNR (signal-noise ratio) is selected as the research object, and the measuring function based on SVD (singular value decomposition) that is not dependent on prior knowledge is proposed as the evaluation function to optimize the parameters of stochastic resonance system. The nature of the stochastic resonance is first described from the eigenspace of the signal. After the analysis of the effects of different system parameters, amplitude normalization is employed to optimize only one parameter, simplifying the algorithm. Finally, an adaptive parameter-tuning stochastic resonance method based on AFSA (artificial fish swarm algorithm) is developed for three types of modulated signals, achieving an optimum matching of noisy signals and non-linear systems at fast convergence speed. According to the simulation, the proposed algorithm is proven effective, efficient, and robust, laying a solid foundation for the subsequent signal processing work.

Adaptive parameter-matching method of SR algorithm for fault diagnosis of wind turbine bearing

The fault diagnosis of wind turbine bearings is challenging because of the heavy background noise and changes in wind speed. Stochastic resonance (SR) is an effective method of detecting fault signal from noise. However, the benefit of SR is seriously limited by the system parameters and the frequency of the input signal. A novel fault diagnosis method for wind turbine bearings, combining an adaptive SR algorithm that is based on quantum particle swarm optimization (QPSO) and frequency conversion based on frequency information exchange (FIE), is proposed. First, the frequency information of the fault characteristic signal is exchanged with the reference frequency by FIE, which can eliminate the limitation of the frequency band. Then, the SR system parameters are optimized by QPSO to avoid blind parameter selection. The signal after FIE is processed by the optimized SR system. The results of case study show that under the same input signal, the proposed method can achieve better signal-to-noise ratio and response amplitude than can the traditional double-side band modulation method and an SR method that is combined only with an optimization algorithm.

The phenomenon of tristable stochastic resonance driven by $$\alpha $$ α -noise

In this paper, the tristable stochastic resonance (SR) phenomenon induced by $$\alpha $$ -stable noise is analysed. The mechanism for realizing resonance is explored based on research concerning the potential function and resonant output of a system. The rules for resonance system parameters q, p, skewness parameter r and intensity amplification factor Q of $$\alpha $$ -stable noise to act on the resonant output are explored under different values of stability index $$\alpha $$ and asymmetric skewness $$\beta $$ of $$\alpha $$ -stable noise. The results will contribute to a reasonable selection of parameter-induced tristable SR system parameters under $$\alpha $$ -stable noise, and lay the foundation for a practical engineering application of weak signal detection based on the SR.

An adaptive stochastic resonance method based on grey wolf optimizer algorithm and its application to machinery fault diagnosis

Stochastic resonance (SR) is widely used as an enhanced signal detection method in machinery fault diagnosis. However, the system parameters have significant effects on the output results, which makes it difficult for SR method to achieve satisfactory analysis results. To solve this problem and improve the performance of SR method, this paper proposes an adaptive SR method based on grey wolf optimizer (GWO) algorithm for machinery fault diagnosis. Firstly, the SR system parameters are optimized by the GWO algorithm using a redefined signal-to-noise ratio (SNR) as optimization objective function. Then, the optimal SR output matching the input signal can be adaptively obtained using the optimized parameters. The proposed method is validated on a simulated signal detection and a rolling element bearing test bench, and then applied to the gear fault diagnosis of electric locomotive. Compared with the conventional fixed-parameter SR method, the adaptive SR method based on genetic algorithm (GA-SR) as well as the well-known fast kurtogram method, the proposed method can achieve a greater accuracy. The results indicated that the proposed method has great practical values in engineering.

Rolling Bearing Fault Signal Extraction Based on Stochastic Resonance-Based Denoising and VMD

Aiming at the difficulty of early fault vibration signal extraction of rolling bearing, a method of fault weak signal extraction based on variational mode decomposition (VMD) and quantum particle swarm optimization adaptive stochastic resonance (QPSO-SR) for denoising is proposed. Firstly, stochastic resonance parameters are optimized adaptively by using quantum particle swarm optimization algorithm according to the characteristics of the original fault vibration signal. The best stochastic resonance system parameters are output when the signal to noise ratio reaches the maximum value. Secondly, the original signal is processed by optimal stochastic resonance system for denoising. The influence of the noise interference and the impact component on the results is weakened. The amplitude of the fault signal is enhanced. Then the VMD method is used to decompose the denoised signal to realize the extraction of fault weak signals. The proposed method was applied in simulated fault signals and actual fault signals. The results show that the proposed method can reduce the effect of noise and improve the computational accuracy of VMD in noise background. It makes VMD more effective in the field of fault diagnosis. The proposed method is helpful to realize the accurate diagnosis of rolling bearing early fault.

Lowering the signal‐to‐noise ratio wall for energy detection using parameter‐induced stochastic resonator

Energy detection is the most frequently used spectrum sensing technique, however, in uncertain low signal-to-noise ratio (SNR) conditions, it generally suffers from the `SNR wall', that is, a minimum SNR below which it is impossible to reliably detect a signal. To address this issue, an improved energy detection (IED) algorithm based on non-linear parameter-induced stochastic resonator (PSR) is proposed in this study. By adopting the method which combines classic adiabatic approximation stochastic resonance (SR) theory and non-classic parameter-induced SR theory, an analytical expression of SR system parameters is derived. On this basis, a PSR is proposed which is mathematically testified to have the ability to improve the SNR of the received signal. Further, an IED algorithm is proposed by introducing the PSR as the preprocessor of energy detection. Theoretical analyses and simulation results prove that a significantly detection performance and SNR wall improvement can be achieved using the proposed IED algorithm.

A Novel Parameter-Tuned Stochastic Resonator for Binary PAM Signal Processing at Low SNR

To improve the receiving performance of binary pulse amplitude modulated (BPAM) signal at low signal-to-noise ratio (SNR), a novel parameter-tuned stochastic resonator (PSR) is proposed. In this letter, we propose a new method for PSR which combines the conventional stochastic resonance (SR) approach based on classic adiabatic approximation theory with the non-conventional SR approach based on parameter tuned theory. Based on this method, we derive an analytical expression of the parameters of SR system. By tuning the parameters of SR system according to the proposed method, there exists a cooperative effect that the BPAM signal is enhanced by the background noise, and the PSR-based receiver is easy to be implemented. Simulation results demonstrate that the bit error ratio (BER) performance of the nonlinear PSR-based receiver is superior to the linear optimal receiver at low SNR.