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

Li, Bing; Li, Jimeng; He, Zhengjia

doi:10.1007/s11431-011-4612-9

Cited by 35 publications

(11 citation statements)

References 27 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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“…Although SR has a distinct advantage in the weak signal processing, when the SNR of vibration signals is low, the detection effect of single SR is not satisfactory [25]. To obtain higher SNR, the Cascaded Stochastic Resonance (CSR) was proposed to increase the output SNR [26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

A new weak fault diagnosis method based on multi-scale wavelet noise tuning cascaded multi-stable stochastic resonance

Han¹,

An²,

Shi³

et al. 2018

J. vibroeng.

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For detecting the weak fault diagnosis submerged in heavy noise, a new method called multi-scale cascaded multi-stable stochastic resonance (MCMSR) is studied. The method can effectively extract weak fault diagnosis from noise background using multi-scale wavelet noise tuning stochastic resonance (SR). Firstly, input signal with noise is decomposed by multi-scale wavelets transformation, and each scale signal is adjusted by scaling factor, then the decomposed signal is used as the input of cascaded multi-stable systems to achieve the detection of fault diagnosis. If the input signal is a large parameter signal, to conform to the conditions of SR, the decomposed signal must be processed by twice sampling. The simulation and experimental signals are carried out to test the feasibility of the method. From the signal to noise ratio (SNR) comparison curves of original signal, SR output signal and MCMSR output signal plotted together, we can find that the useful signal can be enhanced by MCMSR method than SR method. The experimental results indicate that the MCMSR can extract fault diagnosis from heavy background noise.

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“…In order to settle the problems mentioned above, namely, further improving the weak feature extraction performance, some works based on cascaded SR [19][20][21][22][23][24][25] have done until now. As a result, the weak feature extraction performance of cascaded SR has evident improvement compared with that of the single SR.…”

Section: Introductionmentioning

confidence: 99%

Improving the weak feature extraction by adaptive stochastic resonance in cascaded piecewise-linear system and its application in bearing fault detection

Liu¹,

Han²,

Yang³

et al. 2017

J. vibroeng.

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In mechanical engineering field, early fault features are extremely weak and submerged in heavy noise, and the weak feature extraction is quite challenging. In this work, we apply the adaptive stochastic resonance in cascaded piecewise-linear system to extract the weak features. The adaptive stochastic resonance is realized by the quantum particle swarm algorithm. By optimizing system parameters, the efficiency of the feature extraction is improved greatly. As a result, the weak features can be easily extracted eventually. The effectiveness and the high-performance of the proposed method are verified by the numerical simulation and experimental data of rolling element bearings. The bearing fault under different motor loads is detected effectively, consequently confirming the robustness of the proposed method.

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

Cited by 35 publications

References 27 publications

Periodic fault signal enhancement in rotating machine vibrations via stochastic resonance

Periodic fault signal enhancement in rotating machine vibrations via stochastic resonance

A new weak fault diagnosis method based on multi-scale wavelet noise tuning cascaded multi-stable stochastic resonance

Improving the weak feature extraction by adaptive stochastic resonance in cascaded piecewise-linear system and its application in bearing fault detection

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