Multi-stable stochastic resonance and its application research on mechanical fault diagnosis

Li, Jimeng; Chen, Xuefeng; He, Zhengjia

doi:10.1016/j.jsv.2013.06.017

Cited by 132 publications

(65 citation statements)

References 28 publications

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“…The fault-induced impulses are different from the simple sinusoids as they present nonstationary and nonlinear features (Randall and Antoni, 2011;Li et al, 2013b). Hence, the proposed novel approach which is committed to practical fault diagnosis application addresses the impulse signals rather than the sinusoids.…”

Section: Scheme Of the Proposed Approachmentioning

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: Scheme Of the Proposed Approachmentioning

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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“…System parameters analysis results, (a) output SNR and ISNR with different calculating step h increasing from 0.005 to 0.1, A amp ¼ 2, and ¼ À0:2 for ASR, (b) output SNR and ISNR with different amplification factor A amp increasing from 1 to 10,, h ¼ 0:02 and ¼ À0:2 for ASR, (c) output SNR and ISNR with different degree of asymmetry increasing from -0.7 to 0.1, A amp ¼ 3, and h ¼ 0:02, (d) joint impacts of h and A amp on output ISNR. frequency f 0 ¼ 10 Hz and intrinsic frequency f m ¼ 2000 Hz according to equation (10). Noise with intensity of ffiffiffiffiffiffi ffi 2D p ¼ 1:5 is added with calculated SNR ¼ À25:99 dB and ISNR ¼ À 18:00 dB.…”

Section: Performance Of Svasr With Unilateral Impulsive Signal Systemmentioning

confidence: 99%

Weak impulsive signals detection based on step-varying asymmetric stochastic resonance

Zhang

Zheng

Kong

2016

Proceedings of the Institution of Mechanical Engineers, Part C:

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Rotating machinery response is often characterized by the presence of periodic impulses modulated by high-frequency components. The fault information is often hidden in its envelope signal which is unilateral when demodulated. Conventional stochastic resonance with a symmetric potential cannot always contain the signal's original features especially the asymmetry. In this article, a step-varying asymmetric stochastic resonance system for impulsive signal denoising and recovery as well as the rotating machine fault diagnosis is proposed to further improve the impulsive signal-to-noise ratio. In the method, the asymmetry of step-varying asymmetric stochastic resonance can match the unilateral impulsive signal well to generate an optimal dynamic system by selecting proper system parameters and degree of asymmetry. Systems with different simulated or experimental signals are also studied to verify its effectiveness and availability. Results indicate that the step-varying asymmetric stochastic resonance performs much better in detection of impulsive signal than the conventional stochastic resonance with merits of good frequency response, anti-noise capability, adaptability to asymmetric signal and original waveform preserving.

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“…SR mechanism implies that, if a sinusoidal driving frequency mixed with noise is given as input to a nonlinear system, its output contains a high peak corresponding to the driving frequency which varies its amplitude as a function of the noise added through the system [3,4].…”

Section: The Theory Of Srmentioning

confidence: 99%

“…Several techniques exist and are applied for the detection of defects in rotating machines such as gears or bearings in many industrial applications, but SR is the only one that takes advantage of noise. In fact generally mechanical acquisitions are strongly corrupted by background noise from other elements of the system and this component is usually neglected, on the contrary it is used by SR to enhance the features of faults [3].…”

Section: Introductionmentioning

confidence: 99%

Stochastic Resonance algorithms to enhance damage detection in bearing faults

Castiglione

Garibaldi

Marchesiello

2015

MATEC Web of Conferences

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Abstract. Stochastic Resonance is a phenomenon, studied and mainly exploited in telecommunication, which permits the amplification and detection of weak signals by the assistance of noise. The first papers on this technique are dated early 80 s and were developed to explain the periodically recurrent ice ages. Other applications mainly concern neuroscience, biology, medicine and obviously signal analysis and processing. Recently, some researchers have applied the technique for detecting faults in mechanical systems and bearings. In this paper, we try to better understand the conditions of applicability and which is the best algorithm to be adopted for these purposes. In fact, to get the methodology profitable and efficient to enhance the signal spikes due to fault in rings and balls/rollers of bearings, some parameters have to be properly selected. This is a problem since in system identification this procedure should be as blind as possible. Two algorithms are analysed: the first exploits classical SR with three parameters mutually dependent, while the other uses Woods-Saxon potential, with three parameters yet but holding a different meaning. The comparison of the performances of the two algorithms and the optimal choice of their parameters are the scopes of this paper. Algorithms are tested on simulated and experimental data showing an evident capacity of increasing the signal to noise ratio.

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Multi-stable stochastic resonance and its application research on mechanical fault diagnosis

Cited by 132 publications

References 28 publications

Periodic fault signal enhancement in rotating machine vibrations via stochastic resonance

Periodic fault signal enhancement in rotating machine vibrations via stochastic resonance

Weak impulsive signals detection based on step-varying asymmetric stochastic resonance

Stochastic Resonance algorithms to enhance damage detection in bearing faults

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