Adaptive Stochastic Resonance Research Articles

Weak fault feature extraction for polycrystalline diamond compact bit based on ensemble empirical mode decomposition and adaptive stochastic resonance

Aiming at the problem of weak early fault signals of rotating machinery, a feature extraction method combining ensemble empirical mode decomposition (EEMD) and adaptive stochastic resonance (ASR) is proposed. First, the original vibration signal is decomposed into a series of intrinsic mode functions (IMF) through EEMD, and the main IMF components are selected using the correlation coefficient and the root mean square principle. Next, the selected components are reconstructed and used as the input of the ASR system based on the gray wolf algorithm. Finally, to evaluate the performance of ASR, a new evaluation index-weighted power spectrum kurtosis (WPSK) is defined. The results of experimental analysis on the simulation signals and vibration signals are used to verify the feasibility and superiority of the proposed method. Compared with the GA-SR, the proposed method increases the WPSK value by 34.5% when detecting weak signals of worn polycrystalline diamond compact bits.

An adaptive fractional stochastic resonance method based on weighted correctional signal-to-noise ratio and its application in fault feature enhancement of wind turbine

Stochastic resonance (SR) is an effective tool to enhance weak signal by utilizing noise to reach a certain synergistic effect, which has been widely studied in the field of weak signal detection. Currently, using SR to enhance the weak fault feature of wind turbine faces two challenges: First, it is difficult for SR to select the optimal system parameters, while the traditional adaptive method based on SNR needs to predict the precise frequency of the target signal. Second, the wind turbine load changes frequently, making the vibration and noise large. As a result, the traditional SR cannot effectively highlight the target fault feature by inducing a stable resonance phenomenon at the target frequency. To improve the ability of SR to enhance the weak fault feature of wind turbine under strong noise, this paper proposes an adaptive fractional SR method based on weighted correctional signal-to-noise ratio (WCSNR). Firstly, the proposed method considers the adiabatic approximation applicable condition in the SR system and combines characteristics of the expected output signal to construct the WCSNR evaluation index to quantify the system output response, so that the system can adaptively obtain optimal parameters without predicting the accurate frequency of the target signal. Then, the fractional-order theory is applied to the SR system to overcome the shortcoming that the integer-order SR cannot induce stable resonance phenomenon at the target frequency when enhancing the fault feature of wind turbine, and use WCSNR to search for the optimal fractional order to further enhance the weak fault characteristics. Simulation and engineering actual data analysis results verify the effectiveness and superiority of the proposed method in the fault feature enhancement of wind turbine. The analysis results show that compared with the traditional SR method, the method proposed in this paper can more effectively reduce the interference of background noise and accurately enhance the weak fault feature.

Research on Fault Feature Extraction of Hydropower Units Based on Adaptive Stochastic Resonance and Fourier Decomposition Method

In order to effectively extract the characteristics of nonstationary vibration signals from hydropower units under noise interference, an adaptive stochastic resonance and Fourier decomposition method (FDM) based on genetic algorithm (GA) are proposed in this paper. Firstly, GA is used to optimize the resonance parameters so that the signal can reach the optimal resonance and the signal-to-noise ratio (SNR) can be improved. Secondly, FDM is used to process the signal and the appropriate frequency band function is selected for reconstruction. Finally, Hilbert envelope demodulation analysis was performed on the reconstructed signal to obtain the fault characteristics from the envelope spectrum. In order to prove the effectiveness and superiority of the proposed method, comparative experiments are designed by using the simulated signal and the measured swing signal of a hydropower unit. The results show that this method can effectively remove the noise interference and improve the SNR and extract the characteristic frequency of the signal, which has the extensive engineering application value to the fault diagnosis of hydropower units.

Maximum cross-correlated kurtosis-based unsaturated stochastic resonance and its application to bearing fault diagnosis

Considering the random impulses of mechanical noise and the limitations involved while identifying mechanical fault impulse signals via traditional measurement indices of signal-to-noise ratio, which require the characteristic frequency to be known in advance, this study proposes an adaptive unsaturated stochastic resonance method employing maximum cross-correlated kurtosis as the signal detection index. The proposed method combines the features of a cross-correlated coefficient to indicate periodic fault transients and those of spectrum kurtosis to locate these transients in the frequency domain. Actual vibration signals collected from motor and gear bearings subjected to heavy noise are used to demonstrate the effectiveness of the proposed method. Through a coarse tree-based machine learning method, the proposed method is verified to be more suitable for explaining the periodic impulse components of bearing signals, as compared to the ensemble empirical mode decomposition denoising method and unsaturated stochastic resonance using the kurtosis-intercorrelation index.

Bearing Fault Diagnosis Using Synthetic Quantitative Index-Based Adaptive Underdamped Stochastic Resonance

Stochastic resonance is like a nonlinear filter to detect the weak bearing fault-induced impulses that submerged in strong noises. Signal-to-noise ratio (SNR) is often used as the index to evaluate the SR output, but the fault characteristic frequency (FCF) must be known in order to calculate SNR. A novel bearing fault diagnosis method called synthetic quantitative index-based adaptive underdamped stochastic resonance (SQI-AUSR) is proposed. The synthetic quantitative index (SQI) is composed of power spectrum kurtosis, kurtosis, margin index, and correlation coefficient. The SQI is independent of FCF, which avoids the limitation that the calculation of SNR must know the FCF. Numeric simulations and two case studies of bearing faults are carried out. The results show that (1) the SQI is more effective than other proposed indexes such as correlation coefficient and weight power spectrum kurtosis and (2) the proposed SQI-AUSR is effective for bearing fault diagnosis and is better than SNR-AOSR.

Recovery and enhancement of unknown aperiodic binary signal by adaptive aperiodic stochastic resonance

In this study, the system with fractional power nonlinearity is introduced into the theory of aperiodic stochastic resonance (ASR). The fractional exponent is a key parameter and its effect on the ASR phenomenon excited by aperiodic binary signal is investigated in this system. Compared to the classical bistable system, the system with fractional power nonlinearity shows better performance. It can adjust not only the noise intensity but also the fractional exponent to enhance weak signal. In the field of signal transmission, pure aperiodic binary signal is usually submerged in the noise and the signal is unknown. Thus, an effective method is proposed based on ASR and moving average. By the method, the unknown aperiodic binary signal can be recovered in the noise background. To improve the efficiency of the signal recovery, the adaptive ASR is realised with the help of adaptive particle swarm optimisation (APSO) algorithm to optimise the parameters. The method may provide some reference to the engineering field.

Adaptive stochastic resonance in bistable system driven by noisy NLFM signal: phenomenon and application.

The stochastic resonance (SR) in a bistable system driven by nonlinear frequency modulation (NLFM) signal and strong noise is studied. Combined with empirical mode decomposition (EMD) and piecewise idea, an adaptive piecewise re-scaled SR method based on the optimal intrinsic mode function (IMF), is proposed to enhance the weak NLFM signal. At first, considering the advantages of EMD for dealing with non-stationary signals, the segmented NLFM signal is processed by EMD. Meanwhile, the cross-correlation coefficient is used as the measure to select the optimal IMF that contains the NLFM signal feature. Then, the spectral amplification gain indicator is proposed to realize the adaptive SR of the optimal IMF of each sub-segment signal and reconstruct the enhanced NLFM signal. Finally, the effectiveness of the proposed method is highlighted with the analysis of the short-time Fourier transform spectrum of the simulation results. As an application example, the proposed method is verified adaptability in bearing fault diagnosis under the speed-varying condition that represents a typical and complicated NLFM signal in mechanical engineering. The research provides a new way for the enhancement of weak non-stationary signals. This article is part of the theme issue 'Vibrational and stochastic resonance in driven nonlinear systems (part 1)'.

Adaptive Modulation Stochastic Resonance of Two-Dimensional Asymmetric Bistable System and its Application in Bearing Fault Diagnosis

A two-dimensional asymmetric bistable stochastic resonance (TABSR) system model is proposed, and the parameter optimization is performed adaptively by genetic algorithm (GA). In essence, the model is coupled by a monostable model with an asymmetric piecewise-linear model utilizing linear coupling. The monostable model is used as the control system, and the asymmetric piecewise-linear model is used as the controlled system. A set of parameters of the controlled system is firstly obtained, so the TABSR system model can achieve better performance by adjusting the parameters and coupling coefficients of the control system. The output signal-to-noise ratio (SNR) formula of the two-dimensional model is derived and compared with the asymmetric piecewise-linear stochastic resonance (APSR) model. Then the effects of system parameters and coupling coefficients of the control system on system performance are explored. Through numerical simulation, the correctness of the theoretical analysis is verified, and the system is proved to have high theoretical research value. Finally, TABSR is applied to industrial bearing fault diagnosis to recognize weak fault signals which shows the practical application value, and the comprehensive performance comparison with APSR is conducted to validate the advancement of TABSR.

A Novel Adaptive Stochastic Resonance Method Based on Tristable System and its Applications

Stochastic resonance systems have the advantages of converting noise energy into signal energy, and have great potential in the field of signal detection and extraction. Aiming at the problems of the performance of classical stochastic resonance system whose model is not perfect enough and the correlation coefficients between parameters is too large to be optimized by algorithm, then a novel model of the tristable potential stochastic resonance system is proposed. The output SNR formula of the model is derived and analyzed, and the influence of its parameters on the model is clarified. Compared with the piecewise linear model by numerical simulation, the correctness of the formula and the superiority of the model are verified. Finally, the model and the classical tristable model are applied to bearing fault detection in which the genetic algorithm is used to optimize the parameters of the two systems. The results show that the model has better detection effects, which prove that the model has a strong potential in the field of signal detection.

An adaptive empirical mode decomposition and stochastic resonance system in high efficient detection of terahertz radar signal

Interference noise affects the ranging capability of terahertz radar signal detection. Aiming at the above problem, a method based on adaptive empirical mode decomposition and stochastic resonance system is proposed. With the preprocessing method of twice sampling, adaptive empirical mode decomposition, stochastic resonance system, and scale recovery, the optimal intrinsic mode function can be screened automatically, the optimal parameters can be obtained automatically and the ranging calculation can be completed. Experimental results demonstrate the effectiveness and superiority of the proposed adaptive system in achieving weak fault signal detection under heavy background noise.

Vibration-based fault diagnosis of the natural gas compressor using adaptive stochastic resonance realized by Generative Adversarial Networks

The compressor as an important energy transmission equipment is widely used in the natural gas pipelines to pressurize natural gas, which is prone to fail due to the characteristics of high pressure, flammability and corrosion of the natural gas. The faulty compressors may cause natural gas leakage, transmission interruption, and even explosion. Vibration monitoring and fault diagnosis of the natural gas compressor is an effective technology to ensure its security. Due to the strong noise interference, the traditional vibration diagnostic methods are difficult to obtain better effects. Stochastic resonance (SR) is a useful technology that can make full use of the noise components contained in the vibration signals to enhance the weak fault features to complete fault diagnosis. However, the applications of SR in fault diagnosis are faced with many problems, such as the difficulty of parameter selection and the need for intensive data. To solve them, an adaptive generation model of SR parameters is established by using Generative Adversarial Networks (GAN), an adaptive SR-GAN method is accordingly proposed for completing the fault diagnosis of a natural gas compressor. The simulation experiment and field data analysis are adopted to verify the effectiveness of the proposed method. The results indicate that the proposed method can improve diagnostic accuracy by 2.07% compared to the traditional adaptive SR method realized by multilayer perceptron neural network, and improve the signal-to-noise ratio by 2.25 dB at most compared with the other three methods. Moreover, the proposed method still has higher accuracy under the condition of small sample sizes. The proposed method can expand the sample when the sample size is smaller, so that the model can contain completely the operating conditions to improve the accuracy of fault diagnosis. It provides a new idea for the vibration monitoring of natural gas compressors.

An adaptive smooth unsaturated bistable stochastic resonance system and its application in rolling bearing fault diagnosis

An adaptive smooth unsaturated bistable stochastic resonance (ASUBSR) system for bearing fault signal detection is established. Based on the problem of output saturation and poor low-frequency suppression performance of classical bistable stochastic resonance (CBSR) system, an SUBSR with unsaturated characteristics is proposed. An ASUBSR system is designed by extracting the envelope spectrum of the input signal and resampling it to satisfy the adiabatic approximation condition, combining high-pass filter to filter out low-frequency interference, and using genetic algorithm to select the optimal system parameters. Through simulations and experiments, we found that the system can effectively suppress the interference of low-frequency and high-frequency, indicates that the system performs like a band-pass filter, and the output signal-to-noise ratio is better than that of the CBSR system. The proposed ASUBSR system has great application in the field of fault detection of rolling bearings.

Stochastic resonance induced by an unknown linear frequency modulated signal in a strong noise background.

Stochastic resonance (SR) is widely used as a signal enhancement technique in recovering and enhancing periodic or aperiodic signals submerged in noise. However, system parameters and noise intensity tend to influence the SR performance. To achieve better resonance performance, several indices are often used to determine these parameters, including signal-to-noise, amplification factor, and cross-correlation coefficient. Nevertheless, for a linear frequency modulated (LFM) signal, such indices may no longer work and consequently make SR unable to recover the unknown LFM signal from raw signals. Thus, this limits the application of SR to some extent. To deal with this problem, we define here a new index to characterize the unknown LFM signal with the help of the fractional Fourier transform. Guided by this index, SR is thus able to recover the unknown LFM signal from the raw signal. In addition, a cloud model based genetic algorithm is used to achieve an adaptive SR in order to improve the effectiveness of signal processing.

Adaptive monostable stochastic resonance for processing UV absorption spectrum of nitric oxide.

When ultraviolet (UV) absorption spectroscopy technology is used for nitric oxide (NO) detection, the background noise will directly affect the accuracy of concentration inversion, especially in low concentrations. Traditional processing methods attempt to eliminate background noise, which damages the absorption spectrum characteristics. However, stochastic resonance (SR) can utilize the noise to extract a weak characteristic signal. This paper reports a monostable stochastic resonance (MSR) model for processing an UV NO absorption spectrum. By analyzing the characteristics of UV absorption spectrum of NO, the evaluation indexes were constructed, thereby an adaptive MSR method was designed for parameter optimization. The numerical simulation confirmed the absorbance peak can be amplified and spectral signal-to-noise ratio (SNR) can be in the stable range of the proposed method, when noise intensity increased. Finally, this experiment obtained a NO detection limit (3σ) of 1.456 ppm and the maximum relative deviation of concentration is 6.32% by this proposed method, which is satisfactory for processing of the UV NO absorption spectrum.

Actuator Weak Fault Diagnosis in Autonomous Underwater Vehicle Based on Tri-Stable Stochastic Resonance

The autonomous underwater vehicle (AUV) is mainly used in the development and exploration of the ocean. As an important module of the AUV, the actuator plays an important role in the normal execution of the AUV. Therefore, the fault diagnosis of the actuator is particularly important. At present, the research on the strong faults, such as the winding of the actuator, has achieved good results, but the research on the weak fault diagnosis is relatively rare. In this paper, the tri-stable stochastic resonance model is analyzed, and the ant colony tri-stable stochastic resonance model is used to diagnose the weak fault. The system accurately diagnoses the fault of the actuator collision and verifies the adaptive tri-stable stochastic resonance system. This model has better diagnostic results than the bi-stable stochastic resonance system.

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.

Optimal IMF selection and unknown fault feature extraction for rolling bearings with different defect modes

Rolling bearings are widely used in the rotating machinery. The fault types and fault feature frequencies are usually unknown when rolling bearings fail in the engineering applications, which brings difficulties to accurately recognize the health status of rolling bearings. Realizing the extraction of unknown fault features is of great significance for rolling bearing fault diagnosis in real engineering scenarios. Based on the ensemble empirical mode decomposition (EEMD) and stochastic resonance (SR), an adaptive fault diagnosis method for unknown rolling bearing faults is proposed by designing an effective frequency cut-off criteria and constructing two novel evaluation indicators. For a target signal containing complex frequency components, a frequency cut-off criterion is applied to remove redundant intrinsic mode function (IMF) components and improve the accuracy of unknown fault diagnosis in the process of using EEMD to decompose target signal. For the remaining effective IMFs, the spectral amplification factor (SAF) index is built to estimate the energy of each IMF for selecting the optimal IMF. The adaptive SR with the piecewise mean value (PMV) index is performed to extract the unknown fault features from the optimal IMF. The proposed frequency cut-off criteria obviously reduce the interference of other unknown frequencies. Compared with traditional kurtosis, SAF has good noise immunity and stability. The effectiveness of the proposed method is successfully verified by three rolling bearing experiments with different defect modes. Moreover, the vibration waveform characterizing unknown fault feature is reproduced to some extent based on the adaptive SR response. The proposed method accurately realizes unknown fault diagnosis and provides a reference for condition monitoring of rolling bearings in engineering practice.

Adaptive piecewise re-scaled stochastic resonance excited by the LFM signal

The piecewise re-scaled stochastic resonance method is proposed and thoroughly investigated in a bistable system, which is induced by the linear frequency-modulated (LFM) signal. At first, the theoretical formulation for piecewise re-scaled stochastic resonance is explained in detail. Then, several numerical simulations are carried out and the effects of some related parameters are discussed, in which the moment of the signal segmentation and the re-scaled coefficient are key factors. Meanwhile, the numerical results indicate that the proposed method manages to process the LFM signal submerged in the noise. After that, adaptive piecewise re-scaled SR is proposed to solve the problem of the parameter selection. At last, the comparison between fractional Fourier transform (FRFT) and the proposed method is present. Compared to the traditional FRFT, the method has a better performance, especially in amplification effect. The method in this paper may provide reference for processing other kinds of frequency-modulated signals besides the LFM signal.

Weak Fault Diagnosis of Rolling Bearing Based on Improved Stochastic Resonance

Stochastic resonance can use noise to enhance weak signals, effectively reducing the effect of noise signals on feature extraction. In order to improve the early fault recognition rate of rolling bearings, and to overcome the shortcomings of lack of interaction in the selection of SR (Stochastic Resonance) method parameters and the lack of validation of the extracted features, an adaptive genetic random resonance early fault diagnosis method for rolling bearings was proposed. compared with the existing methods, the AGSR (Adaptive Genetic Stochastic Resonance) method uses genetic algorithms to optimize the system parameters, and further optimizes the parameters while considering the interaction between the parameters. This method can effectively extract the weak fault features of the bearing. In order to verify the effect of feature extraction, the feature signal extracted by AGSR method was input into the Fully connected neural network for fault diagnosis. the practicality of the algorithm is verified by simulation data and rolling bearing experimental data. the results show that the proposed method can effectively detect the early weak features of rolling bearings, and the fault diagnosis effect is better than the existing methods.

A Piecewise Hybrid Stochastic Resonance Method for Early Fault Detection of Roller Bearings

An unsaturated stochastic resonance (USR) method to overcome the output saturation phenomenon observed in the classical bistable stochastic resonance (CBSR) method has been examined. However, the parameters of USR models can lead to inaccurate results while identifying the characteristic frequency amid high levels of background noise. To overcome this limitation, an adaptive piecewise hybrid stochastic resonance (APHSR) method that introduces a parameter μ to improve the performance of fault characteristic detection is proposed. The optimal parameters are determined automatically using both 3D reverse positioning and least-squares methods, combing with signal-to-noise ratio and spectral value as evaluation criteria. The significance of parameter μ is demonstrated by analyzing the critical amplitude and Kramers' escape rate. When the results were evaluated through comparison with the CBSR and USR methods via a simulation and two experiments on a motor and a parallel gearbox, it was demonstrated to be more capable of diagnosing the early faults of rotating machinery especially in high levels of background noise.