Extended stochastic resonance (SR) and its applications in weak mechanical signal processing

Hu, Niaoqing; Chen, Min; Qin, Guanshi; Xia, Lurui; Pan, Zhongyin; Feng, Zhanhui

doi:10.1007/s11465-009-0072-3

Cited by 10 publications

(4 citation statements)

References 8 publications

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“…However, blindly increasing the step will cause the output to diverge, resulting in detection distortion. To ensure that the output converges, the system parameters and calculation step must satisfy the following constraint [42]:…”

Section: Bistable Stochastic Resonancementioning

confidence: 99%

Multiscale noise suppression and feature frequency extraction in SSVEP based on underdamped second-order stochastic resonance

Yao

Jia

et al. 2019

J. Neural Eng.

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Objective. As one of the commonly used control signals of brain-computer interface (BCI), steady-state visual evoked potential (SSVEP) exhibits advantages of stability, periodicity and minimal training requirements. However, SSVEP retains the non-linear, non-stationary and low signal-to-noise ratio (SNR) characteristics of EEG. The traditional SSVEP extraction methods regard noise as harmful information and highlight the useful signal by suppressing the noise. In the collected EEG, noise and SSVEP are usually coupled together, the useful signal is inevitably attenuated while the noise is suppressed. Also, an additional band-pass filter is needed to eliminate the multi-scale noise, which causes the edge effect. Approach. To address this issue, a novel method based on underdamped second-order stochastic resonance (USSR) is proposed in this paper for SSVEP extraction. Main results. A synergistic effect produced by noise, useful signal and the nonlinear system can force the energy of noise to be transferred into SSVEP, and hence amplifying the useful signal while suppressing multi-scale noise. The recognition performances of detection are compared with the widelyused canonical coefficient analysis (CCA) and multivariate synchronization index (MSI). Significance. The comparison results indicate that USSR exhibits increased accuracy and faster processing speed, which effectively improves the information transmission rate (ITR) of SSVEP-based BCI.

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Section: Bistable Stochastic Resonancementioning

confidence: 99%

Multiscale noise suppression and feature frequency extraction in SSVEP based on underdamped second-order stochastic resonance

Yao

Jia

et al. 2019

J. Neural Eng.

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“…It is an emerging science to study how to extract weak signals from strong noise. It requires multiple disciplines to work together, so it is also an interdisciplinary [3,4].…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction Method for Weak Faults Based on Time-Delayed Feedback Mixed Potential Stochastic Resonance

Tang

Shi

2020

Shock and Vibration

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To extract weak faults under strong noise, a method for feature extraction of weak faults with time-delayed feedback mixed potential stochastic resonance (TFMSR) is proposed. This method not only overcomes the saturation characteristics of classical bistable stochastic resonance (CBSR), but also verifies a new potential function model. Based on this model, considering the short memory characteristics of the CBSR method, a method is proposed that can add historical information to the negative feedback process of the stochastic resonance (SR). Through the combination of the above two methods, the weak fault extraction under strong background noise is realized. The article analyzes the effects of the delay term, feedback term, and system parameter on the effect of SR and uses the ant colony algorithm (ACA) to optimize the above parameters. Finally, through simulated and engineering experimental results, it is proved that the proposed method has more advantages than the CBSR method in weak fault feature extraction.

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“…In the field of signal transmission, Duan [6] found the parameter-induced SR phenomenon in the baseband binary pulse amplitude modulated signals transmission. In the field of mechanical fault diagnosis, Hu et al [11] studied the detection method of weak characteristic signals based on SR and applied it in the early diagnosis of mechanical faults.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis based on Stochastic Resonance with Cuckoo Search

Chi¹

2018

IJPE

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Rolling bearings are the main components of modern machinery, and harsh operating environments often make them prone to failure. Therefore, detecting the incipient fault as soon as possible is useful for bearing prognostics and health management. However, the useful feature information relevant to the bearing fault contained in the vibration signals is weak under the influence of the noise and transmission path. The useful feature information is even submerged in the noise. Thus, it becomes difficult to identify the fault symptom of rolling bearings in time from the vibration signals. Stochastic resonance (SR) is a reliable method to detect the weak signal in intense noise. However, the effect of SR depends on the adjustment of two parameters. Cuckoo Search (CS) is a heuristic novel optimization algorithm that can search the global solution quickly and efficiently. Thus, CS is utilized to optimize the two parameters in this paper. Local signalto-noise ratio (LSNR) is used to evaluate resonance effect. Two bearing fault datasets were used to confirm the effectiveness of SR optimized by CS. SR methods optimized by particle swarm optimization (PSO), genetic algorithms (GA), firefly algorithm (FA), and ant colony optimization (ACO) are also used to detect the bearing fault signal in the two datasets. The analysis results state SR optimized by CS can find better LSNR than SR optimized by other algorithms no matter if it is in the same iterations or in the same computation time, thereby making the fault feature more obvious.

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Extended stochastic resonance (SR) and its applications in weak mechanical signal processing

Cited by 10 publications

References 8 publications

Multiscale noise suppression and feature frequency extraction in SSVEP based on underdamped second-order stochastic resonance

Multiscale noise suppression and feature frequency extraction in SSVEP based on underdamped second-order stochastic resonance

Feature Extraction Method for Weak Faults Based on Time-Delayed Feedback Mixed Potential Stochastic Resonance

Bearing Fault Diagnosis based on Stochastic Resonance with Cuckoo Search

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