Study on non-linear filter characteristic and engineering application of cascaded bistable stochastic resonance system

Huang, He; Wang, Tao; Leng, Yonggang; Zhang, Ying; Li, Qiang

doi:10.1016/j.ymssp.2007.02.004

Cited by 56 publications

(27 citation statements)

References 23 publications

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“…Hence, the output waveform yielded from the proposed approach is smoother than that from the traditional first-order SR method due to the extra secondary filtering process (x ¼ R R f ðt, xÞdt). Theoretically, the filtering effect can be improved by cascading the SR systems in the DSP-based methods, but it should be noted that the complexity and computation cost will increase at the same time (He et al, 2007;Li et al, 2011). For the proposed approach, the physical structure provides a natural second-order model to realize SR and hence shows superiority as compared to the traditional SR method.…”

Section: Experimental Results Summary and Discussionmentioning

confidence: 99%

Periodic fault signal enhancement in rotating machine vibrations via stochastic resonance

Dai

et al. 2016

Journal of Vibration and Control

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This paper proposes a novel approach to periodic fault signal enhancement in rotating machine vibrations with a tristable mechanical vibration amplifier (TMVA) by exploiting stochastic resonance (SR). The TMVA is a nonlinear physical structure system that consists of a cantilever beam and a magnet system. Through the TMVA, the periodic weak signal can be amplified with the assistance of noise in the regime of SR. Benefitting from a wider interwell spacing and a smoother potential curve, the TMVA produces a more regular output waveform with lower noise in a wider operating bandwidth as compared to the monostable and bistable amplifiers. Different from the traditional signal enhancement approach which is based on digital signal processing (DSP) techniques, the designed physical structure can realize signal enhancement in a simple, intuitive, effective and adaptive way without too much complex operations. The effectiveness and efficiency of the proposed approach are validated by a simulated fault signal and the practical bearing and gearbox fault signals, in comparison with a traditional DSP-based SR method. The principle of the proposed approach shows potential applications on rotating machine fault diagnosis area and other areas related to weak periodic signal enhancement.

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Section: Experimental Results Summary and Discussionmentioning

confidence: 99%

Periodic fault signal enhancement in rotating machine vibrations via stochastic resonance

Dai

et al. 2016

Journal of Vibration and Control

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“…Having a mechanical roll-off of 20dB/decade, these rather high frequencies have negligible impact. He et al [39] showed that by exploiting SR, a non-linear low-pass filter characteristic for the input is introduced, removing high frequency contributions from the added white noise. Thus, adding noise with frequencies larger than the system's resonance frequency does not help to increase the signal-to-noise ratio by SR.…”

Section: Noise Bandwidthmentioning

confidence: 99%

Stochastic Resonance in a Voltage-Controlled Micromechanical Slider

Droogendijk

Boer

Sanders

et al. 2015

J. Microelectromech. Syst.

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Stochastic resonance (SR) is a phenomenon that has been extensively investigated theoretically, but which is much harder to study experimentally. In this paper, we report on an experimental micromechanical slider structure that allows for full control of most of the parameters that are of importance for the occurrence of SR. Using a silicon-on-insulator-based process, we realized a slider structure with periodic capacitive structures to obtain tunable unstable regions. We implemented stochastic resonance by controlling the strength of these capacitive wells by a dc-bias voltage, operating the device in push-pull mode by electrostatic actuation, and adding a judiciously amount of white noise to the actuation comb drives. It is shown that this SR scheme allows for detection of subthreshold forces. We find that the amount of noise needed for optimal SR depends strongly on the noise bandwidth. Further, we demonstrate that the implementation of SR in a slider allows for improved detection of both sinusoidal and triangular waveforms, but that the performance of SR deteriorates for square waveforms.[2014-0083] where he is currently a Senior Technologist. His current research interests include silicon micromachining and related fabrication techniques, with a focus on deep reactive ion etching techniques. He has published over 112 reviewed journal papers on micro (nano) technology and related topics, and holds 10 patent applications. . His research interests include design and fabrication of measurement techniques and setups for a wide range of MEMS structures and microscale chemical reactors. Gijs J. M. Krijnen received the M.Sc. degree in electrical engineering and the Ph.D. degree from the

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“…However, when the amplitude of periodic signal is much more smaller than noise intensity, the detection effect is not satisfactory, and output signal still contains a certain amount of noise and feature of the useful signal is not significant. To further improve detection effect of the weak signal, stochastic resonance enhancement methods have been studied, such as cascade stochastic resonance [29], coupled stochastic resonance [30]. With the intercoupling of adjacent resonance units, coupled SR can interrelate all of the resonance units to improve the output SNR appropriately.…”

Section: Introductionmentioning

confidence: 99%

“…With the intercoupling of adjacent resonance units, coupled SR can interrelate all of the resonance units to improve the output SNR appropriately. In a sense, SR based on a mechanism of energy transition from high-frequency area to low-frequency area to amplify low-frequency signal gradually can be regarded as a special low-pass filter whose filtering effect is better than the conventional low-pass filter [29]. Cascade SR, single bistable system connected in series, not only makes the amplitude of characteristic frequency more outstanding in the frequency domain, but also can weaken high-frequency dithering and make the output waveform smooth in the time domain.…”

Section: Introductionmentioning

confidence: 99%

Fault feature enhancement of gearbox in combined machining center by using adaptive cascade stochastic resonance

2011

Sci. China Technol. Sci.

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The difficulty to select the best system parameters restricts the engineering application of stochastic resonance (SR). An adaptive cascade stochastic resonance (ACSR) is proposed in the present study. The proposed method introduces correlation theory into SR, and uses correlation coefficient of the input signals and noise as a weight to construct the weighted signal-to-noise ratio (WSNR) index. The influence of high frequency noise is alleviated and the signal-to-noise ratio index used in traditional SR is improved accordingly. The ACSR with WSNR can obtain optimal parameters adaptively. And it is not necessary to predict the exact frequency of the target signal. In addition, through the secondary utilization of noise, ACSR makes the signal output waveform smoother and the fluctuation period more obvious. Simulation example and engineering application of gearbox fault diagnosis demonstrate the effectiveness and feasibility of the proposed method. stochastic resonance, adaptive, weighted signal-to-noise ratio, feature enhancement, combined machining center Citation:Li B, Li J M, He Z J. Fault feature enhancement of gearbox in combined machining center by using adaptive cascade stochastic resonance.

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Study on non-linear filter characteristic and engineering application of cascaded bistable stochastic resonance system

Cited by 56 publications

References 23 publications

Periodic fault signal enhancement in rotating machine vibrations via stochastic resonance

Periodic fault signal enhancement in rotating machine vibrations via stochastic resonance

Stochastic Resonance in a Voltage-Controlled Micromechanical Slider

Fault feature enhancement of gearbox in combined machining center by using adaptive cascade stochastic resonance

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