Stochastic resonance with tuning system parameters: the application of bistable systems in signal processing

Xu, Biao; Duan, Fabing; Bao, Rui; Li, Jianlong

doi:10.1016/s0960-0779(00)00266-6

Cited by 162 publications

(92 citation statements)

References 44 publications

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“…In [27], it is used to amplify the coherent signals. As a nonlinear filter, it is also used to recover the multi-frequency signals corrupted by noise [17]. Reducing the bit-error rate (BER) of the binary signal transmission is another application of this system [28].…”

Section: Enhancement Of Stochasticmentioning

confidence: 99%

Enhancement of Stochastic Resonance Using Optimization Theory

Wu¹,

Jiang²,

Repperger³

et al. 2006

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Washington Headquarters Service, Directorate 14. ABSTRACTThe traditional stochastic resonance is realized by adding an optimal amount of noise, while the parameter tuning stochastic resonance is realized by optimally tuning the system parameters. The further improvement of the maximal normalized power norm of the bistable double-well dynamic system with white Gaussian noise input can be converted to an optimization problem with constraints on system parameters and noise intensity, which is proven to have one and only one local maximum for the Gaussian-distributed weak input signal. SUBJECT TERMSSignal-to noise ratio (SNR), suprathreshold stochastic resonance (SSR) Abstract. The traditional stochastic resonance is realized by adding an optimal amount of noise, while the parametertuning stochastic resonance is realized by optimally tuning the system parameters. This paper reveals the possibility to further enhance the stochastic resonance effect by tuning system parameters and adding noise at the same time using optimization theory. The further improvement of the maximal normalized power norm of the bistable double-well dynamic system with white Gaussian noise input can be converted to an optimization problem with constraints on system parameters and noise intensity, which is proven to have one and only one local maximum for the Gaussian-distributed weak input signal. This result is then extended to the arbitrary weak input signal case. For the purpose of practical implementation, a fast-converging optimization algorithm to search the optimal system parameters and noise intensity is also proposed. Finally, computer simulatiolls are performed to verify its validity and demonstrate its potential applications in signal processing.

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Section: Enhancement Of Stochasticmentioning

confidence: 99%

Enhancement of Stochastic Resonance Using Optimization Theory

Wu¹,

Jiang²,

Repperger³

et al. 2006

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show abstract

“…The output SNR will reach a higher maximal value by tuning system parameters than by adjusting noise intensity I. INTRODUCTION [18]. Among this research, either the noise intensity is Stochastic resonance (SR) is the phenomenon that the adjusted or the system parameters are tuned in order to noise can be used to enhance rather than hinder the maximize the chosen measure, but not both.…”

Section: Enhancement Of Stochastic Resonance By Tuning Systemmentioning

confidence: 99%

Enhancement of stochastic resonance by tuning system parameters and adding noise simultaneously

Wu¹,

Jiang²,

Repperger

2006

2006 American Control Conference

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“…Adaptive stochastic resonance (ASR) was first introduced by Mitaim and Kosko in his study on the SR optimality conditions [17]. It can be seen as an improvement of the SR. Based on SR or ASR unique advantage of using noise to enhance weak signals instead of eliminating noise, its application to mechanical fault diagnosis has been studied extensively [18][19][20][21][22][23][24][25][26][27][28][29]. It is very difficult to detect blade fault feature by means of conventional signal processing techniques since the blade crack vibration signal is very weak due to strong noise interference in the practical working centrifugal fan.…”

Section: Introductionmentioning

confidence: 99%

Blade Crack Detection of Centrifugal Fan Using Adaptive Stochastic Resonance

2015

Shock and Vibration

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Centrifugal fans are widely used in various industries as a kind of turbo machinery. Among the components of the centrifugal fan, the impeller is a key part because it is used to transform kinetic energy into pressure energy. Crack in impeller's blades is one of the serious hidden dangers. It is important to detect the cracks in the blades as early as possible. Based on blade vibration signals, this research applies an adaptive stochastic resonance (ASR) method to diagnose crack fault in centrifugal fan. The ASR method, which can utilize the optimization ability of the grid search method and adaptively realize the optimal stochastic resonance system matching input signals, may weaken the noise and highlight weak characteristic and thus can diagnose the fault accurately. A centrifugal fan test rig is established and experiments with three cases of blades are conducted. In comparison with the ensemble empirical mode decomposition (EEMD) analysis and the traditional Fourier transform method, the experiment verified the effectiveness of the current method in blade crack detection.

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Stochastic resonance with tuning system parameters: the application of bistable systems in signal processing

Cited by 162 publications

References 44 publications

Enhancement of Stochastic Resonance Using Optimization Theory

Enhancement of Stochastic Resonance Using Optimization Theory

Enhancement of stochastic resonance by tuning system parameters and adding noise simultaneously

Blade Crack Detection of Centrifugal Fan Using Adaptive Stochastic Resonance

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