Adaptive Stochastic Resonance in Second-Order System with General Scale Transformation for Weak Feature Extraction and Its Application in Bearing Fault Diagnosis

2018 ◽  
Vol 17 (01) ◽  
pp. 1850009 ◽  
Author(s):  
Qiang Ma ◽  
Dawen Huang ◽  
Jianhua Yang
Keyword(s):  
Stochastic Resonance ◽  
Noise Intensity ◽  
Reference Value ◽  
Harmonic Signal ◽  
Second Order ◽  
Bistable System ◽  
Scale Transformation ◽  
Order System ◽  
Weak Signal ◽  
General Scale

The theory of general scale transformation (GST) is put forward and used in the second-order underdamped bistable system to extract weak signal features submerged into strong noise. An adaptive stochastic resonance (SR) with GST is proposed and realized by the quantum particle swarm optimization (QPSO) algorithm. The harmonic signal and experimental signal are considered to compare GST with normalized scale transformation (NST) in the second-order system. The results show that detection effect of the adaptive SR with GST is better than the NST SR. In addition, the output signal-to-noise ratio (SNR) is significantly improved in the GST method. Meanwhile, the dependence of the signal extraction efficiency on the noise intensity is researched. The output SNR is decreased with the increase of the noise intensity in two methods. However, the proposed method is always superior to the NST. Moreover, the superiority of the Brown particle oscillation in the single well is discussed. The proposed method has certain reference value in the extraction of the weak signal under the strong noise background.

An improved adaptive stochastic resonance with general scale transformation to extract high-frequency characteristics in strong noise

2018 ◽  
Vol 32 (15) ◽  
pp. 1850185 ◽  
Author(s):  
Dawen Huang ◽  
Jianhua Yang ◽  
Jingling Zhang ◽  
Houguang Liu
Keyword(s):  
Stochastic Resonance ◽  
High Frequency ◽  
Signal To Noise Ratio ◽  
Scale Transformation ◽  
Weak Signal ◽  
Signal Features ◽  
Signal Characteristics ◽  
Periodic Signals ◽  
Sr Method ◽  
General Scale

The idea of general scale transformation is introduced in detail. Based on this idea, an improved adaptive stochastic resonance (SR) method is proposed to extract weak signal features. Different periodic signals are considered to verify the proposed method. Compared with the normalized scale transformation, the output signal-to-noise ratio (SNR) of the proposed method is increased to a greater extent. Further, the influences of some key parameters on the responses of the two methods are discussed minutely. Results show that the improved adaptive SR method with general scale transformation is obviously superior to the traditional normalized scale transformation that is used in the former literatures. For different noise intensities and time scales, the proposed approach can always obtain the optimal response of SR to enhance the weak signal characteristics.

Aperiodic Stochastic Resonance in the Fractional-Order Bistable System

2019 ◽  
Vol 19 (02) ◽  
pp. 2050014
Author(s):  
Chengjin Wu ◽  
Qing Jiao ◽  
Feng Tian
Keyword(s):  
Stochastic Resonance ◽  
Fractional Order ◽  
Pulse Width ◽  
Technical Problem ◽  
Digital Transmission ◽  
Fractional Order System ◽  
Bistable System ◽  
Scale Transformation ◽  
Order System ◽  
Binary Signal

The aperiodic stochastic resonance (ASR) in the bistable fractional-order system is further studied when the fractional-order lies in the interval (0, 2). In previous works, the ASR can only process aperiodic binary signal with large pulse width. However, for the signal with small pulse width, the method cannot work. Actually, the signals with small pulse width are also common in the information transmission field. It greatly limits the application of the fractional stochastic resonance. Hence, we mainly focus on the enhancement of aperiodic binary signal with small pulse width. To solve the aforementioned technical problem, the general scale transformation is introduced, which allows us to achieve the ASR successfully. By this method, the equivalent system with large system parameters is able to match the input character signal with arbitrary small pulse width well, where the scale parameter is a key to achieve resonance. During the process, the fractional-order system presents rich dynamical behaviors in processing the aperiodic binary signals. Especially, as the order increases, the output signal at optimal noise intensity might be better and be more similar to the input one. This indicates the fractional order can optimize the stochastic resonance. The results might provide some reference to the engineering field, such as digital transmission and image processing field.

scholarly journals Fault Diagnosis Using Cascaded Adaptive Second-Order Tristable Stochastic Resonance and Empirical Mode Decomposition

2021 ◽  
Vol 11 (23) ◽  
pp. 11480
Author(s):  
Hongjiang Cui ◽  
Ying Guan ◽  
Wu Deng
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Empirical Mode Decomposition ◽  
Low Frequency ◽  
Second Order ◽  
Weak Signal ◽  
Ant Colony Optimization Algorithm ◽  
Resonance Theory ◽  
Mode Decomposition ◽  
Diagnosis Method

Aiming at the problems of poor decomposition quality and the extraction effect of a weak signal with strong noise by empirical mode decomposition (EMD), a novel fault diagnosis method based on cascaded adaptive second-order tristable stochastic resonance (CASTSR) and EMD is proposed in this paper. In the proposed method, low-frequency interference components are filtered by using high-pass filtering, and the restriction conditions of stochastic resonance theory are solved by using an ordinary variable-scale method. Then, a chaotic ant colony optimization algorithm with a global optimization ability is employed to adaptively adjust the parameters of the second-order tristable stochastic resonance system to obtain the optimal stochastic resonance, and noise reduction pretreatment technology based on CASTSR is developed to enhance the weak signal characteristics of low frequency. Next, the EMD is employed to decompose the denoising signal and extract the characteristic frequency from the intrinsic mode function (IMF), so as to realize the fault diagnosis of rolling bearings. Finally, the numerical simulation signal and actual bearing fault data are selected to prove the validity of the proposed method. The experiment results indicate that the proposed fault diagnosis method can enhance the decomposition quality of the EMD, effectively extract features of weak signals, and improve the accuracy of fault diagnosis. Therefore, the proposed fault diagnosis method is an effective fault diagnosis method for rotating machinery.

scholarly journals Effects of Second-Order Matched Stochastic Resonance for Weak Signal Detection

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 46505-46515 ◽  
Author(s):  
Haitao Dong ◽  
Haiyan Wang ◽  
Xiaohong Shen ◽  
Zhe Jiang
Keyword(s):  
Signal Detection ◽  
Stochastic Resonance ◽  
Second Order ◽  
Weak Signal ◽  
Weak Signal Detection

An improved adaptive stochastic resonance method for improving the efficiency of bearing faults diagnosis

2017 ◽  
Vol 232 (13) ◽  
pp. 2352-2368 ◽  
Author(s):  
Dawen Huang ◽  
Jianhua Yang ◽  
Jingling Zhang ◽  
Houguang Liu
Keyword(s):  
Stochastic Resonance ◽  
High Frequency ◽  
Signal To Noise Ratio ◽  
Fault Simulation ◽  
Resonance Method ◽  
Bistable System ◽  
Scale Transformation ◽  
High Frequency Signal ◽  
Bearing Fault ◽  
Feature Information

The general scale transformation (GST) method is used in the bistable system to deal with the weak high-frequency signal submerged into the strong noisy background. Then, an adaptive stochastic resonance (ASR) method with the GST is put forward and realized by the quantum particle swarm optimization (QPSO) algorithm. Through the bearing fault simulation signal, the ASR method with the GST is compared with the normalized scale transformation (NST) stochastic resonance (SR). The results show that the efficiency of the GST method is higher than the NST in recognizing bearing fault feature information. In order to simulate the actual engineering environment, both the adaptive GST and the NST methods are implemented to deal with the same experimental signal, respectively. The signal-to-noise ratio (SNR) of the output is obviously improved by the GST method. Specifically, the efficiency is improved greatly to extract the weak high-frequency bearing fault feature information. Moreover, under different noise intensities, although the SNR is decreased versus the increase of the noise intensity, the ASR method with the GST is still better than the traditional NST SR. The proposed GST method and the related results might have referenced value in the problem of weak high-frequency feature extraction in engineering fields.

THE CRITICAL EFFECTS OF TIME DELAY AND NOISE CORRELATION ON STOCHASTIC RESONANCE IN AN ASYMMETRIC BISTABLE SYSTEM

2011 ◽  
Vol 25 (16) ◽  
pp. 1377-1391 ◽  
Author(s):  
ZHENG-LIN JIA ◽  
DONG-CHENG MEI
Keyword(s):  
Time Delay ◽  
Stochastic Resonance ◽  
Noise Intensity ◽  
Signal To Noise Ratio ◽  
State Theory ◽  
Bistable System ◽  
Noise Correlation ◽  
Small Delay ◽  
Correlation Strength ◽  
Two Peaks

We investigate the effects of time delay and noise correlation on the stochastic resonance induced by a multiplicative signal in an asymmetric bistable system. By the two-state theory and small delay approximation, the expression of the output signal-to-noise ratio (SNR) is obtained in the adiabatic limit. The results show that SNR as a function of the multiplicative noise intensity D shows a transition from two peaks to one peak with the decreasing of cross-correlation strength λ and the increasing of delay time τ. Moreover, there are the doubly critical phenomena for SNR versus λ and τ, and SNR versus D and α (additive noise intensity).

Research on Weak Signal Detection by Adding Noise to Cascaded Stochastic Resonance Step by Step

2013 ◽  
Vol 433-435 ◽  
pp. 450-455 ◽  
Author(s):  
Shui Lin Tu ◽  
Zheng Yang Wu ◽  
Zhen Yi Wu
Keyword(s):  
Signal Detection ◽  
Stochastic Resonance ◽  
Transfer Functions ◽  
Bistable System ◽  
Moderate Intensity ◽  
Weak Signal ◽  
Weak Signal Detection ◽  
Potential Wells ◽  
Local Oscillation ◽  
Dc Component

In view of the fact that after excitated by weak signal under background of noise the action of transition between potential wells of bistable system cannot occur or occasionally happen, this paper puts forward a method of weak signal detection by adding noise to cascaded stochastic resonance step by step. In this method the output signal of the former bistable system whose transition between potential wells occasionally occurs is converted into a local oscillation within an unilateral potential well step by step, then DC component is removed, a moderate intensity of Gaussian color noise is stacked and then by utilizing energy transfer functions of bistable system, gradually the signal is enabled to overcome the critical value of the latter bistable system and transition of frequency synchronization between potential wells is produced for signals to be tested. The simulation results of weak sinusoidal signal and periodic impact signals detected under the background of noise show the feasibility of this method.

scholarly journals Stochastic Resonance in Bistable Systems: The Effect of Simultaneous Additive and Multiplicative Correlated Noises

1998 ◽  
Vol 12 (28) ◽  
pp. 1195-1202 ◽  
Author(s):  
Claudio J. Tessone ◽  
Horacio S. Wio
Keyword(s):  
Stochastic Resonance ◽  
Noise Intensity ◽  
Additive Noise ◽  
Signal To Noise Ratio ◽  
Bistable System ◽  
Signal To Noise ◽  
Multiplicative Noises ◽  
Bistable Systems ◽  
Resonance Response ◽  
Correlated Noises

We analyze the effect of the simultaneous presence of correlated additive and multiplicative noises on the stochastic resonance response of a modulated bistable system. We find that when the correlation parameter is also modulated, the system's response, measured through the output signal-to-noise ratio, becomes largely independent of the additive noise intensity.

Stochastic resonance in second-order and underdamped asymmetric bistable system

2015 ◽  
Vol 29 (10) ◽  
pp. 1550034 ◽  
Author(s):  
Yong-Feng Guo ◽  
Ya-Jun Shen ◽  
Jian-Guo Tan
Keyword(s):  
Stochastic Resonance ◽  
Signal To Noise Ratio ◽  
Signal Amplitude ◽  
Exact Expression ◽  
Second Order ◽  
Bistable System ◽  
System Response ◽  
Present Calculation ◽  
Damping Parameter ◽  
Calculation Results

The phenomenon of stochastic resonance (SR) in a second-order and underdamped asymmetric bistable system is investigated. The second-order asymmetric bistable system with Gauss white noise is stochastically equivalent to two-dimensional Markovian process, and the exact expression of the signal-to-noise ratio (SNR) of system response in the presence of weak periodic driving force is obtained under the adiabatic condition and the theory of two-state model intensities. The influences of the damping parameter, asymmetry constant r and the Gaussian white noises on the SNR are discussed. The present calculation results show that, the increase of static asymmetry r and damping parameter γ can restrain the SR phenomenon appears. However, the increase of signal amplitude A can enhance SR.

Medium-Low-Frequency Signal Detection and Simulation Based on the Principle of Stochastic Resonance

2011 ◽  
Vol 105-107 ◽  
pp. 1991-1994
Author(s):  
Wen Li Zhao ◽  
Yuan Ping Yin ◽  
Jin Liu
Keyword(s):  
Signal Detection ◽  
Stochastic Resonance ◽  
Low Frequency ◽  
Harmonic Signal ◽  
Bistable System ◽  
Periodic Signal ◽  
Matlab Simulation ◽  
Resonance Theory ◽  
Simulation Based ◽  
Frequency Components

The principle of stochastic resonance in bistable system is introduced firstly. The medium-low-frequency periodic signal and multi-frequency harmonic signal (the large parameter signal) are common in mechanical failure, but it is difficult to achieve stochastic resonance in these signals detection. The signal modulation characteristic is used in this paper to transform the various frequency components into small parameter signals which satisfy the adiabatic approximation theory. On the basis of that, weak signal detection based on stochastic resonance theory is realized. Then a mixing circuit system based on stochastic resonance is designed, the circuit first makes a frequency selection processing with a mixer on the mixed signal between the measurable signal and a scanning signal, and then it is input to the nonlinear bistable system to realize signal detection based on stochastic resonance. At last, the MATLAB simulation result shows that the circuit can realize the stochastic resonance and detection of weak periodic signal in medium-low frequency from noise background.

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