The Recovery of Weak Impulsive Signals Based on Stochastic Resonance and Moving Least Squares Fitting

Jiang, Kuosheng; Xu, Guanghua; Li, Lin; Tao, Tangfei; Gu, Fengshou

doi:10.3390/s140813692

Cited by 10 publications

(13 citation statements)

References 13 publications

(11 reference statements)

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“…It is suitable for extracting fault features from impulsive and modulating type signals, which can be found in many key machinery components, such as bearings [14], [15], gears [16], turbines [17] and valves [18]. This type of signal is characterised by the presence of a periodic repetition of sharp peaks modulated by high-frequency resonance components [19]. Especially for rolling bearing diagnostics, envelope analysis has been recognised as the benchmark method over many years of development [20], [21].…”

Section: Theoretical Background 21 Envelope Analysis and Its Implemementioning

confidence: 99%

Efficient implementation of envelope analysis on resources limited wireless sensor nodes for accurate bearing fault diagnosis

Feng

Zhao

et al. 2017

Measurement

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With the fast development of electronics and wireless communication technologies in recent years, intelligent wireless sensor nodes are becoming increasingly popular in the online machinery condition monitoring systems. They bring a number of benefits, such as reduced investment on the installation and maintenance of expensive communication cables, ease of deployment and upgrading. For the condition monitoring of dynamic signals, distributed computation on wireless sensor nodes is getting popular with wireless sensor nodes becoming more computation powerful and power efficient. As a widely recognised algorithm for bearing fault diagnosis, envelope analysis has been previously proved suitable for being embedded on the wireless sensor nodes to effectively extract fault features from common machinery components such as bearings and gears. As a continuation, this paper studies into several envelope detection methods, including Hilbert transform, spectral correlation, band-pass squared rectifier and short-time RMS. Regarding to the fact that only low frequency components in the bearing envelope is of interest, spectral correlation can be simplified for fast calculation and short-time RMS method can be considered as a simplified band-pass squared rectifier, in which partial aliasing is allowed. Thereafter, spectral correlation and short-time RMS are employed to speed up the calculation of envelope analysis on a wireless sensor node, which thereafter provides the potential to reduce power consumption of wireless sensor nodes. The computation speed comparison shows that the spectral correlation method and short-time RMS can speed up the computation speed by more than two times and five times in comparison with the Hilbert transform method. The simulation study shows that spectral correlation and shorttime RMS based methods achieves similar level of accuracy as Hilbert transform. Furthermore, the experimental study shows that spectral correlation and short-time RMS based methods can well reveal the simulated three types of bearing faults while with the computation speed significantly improved.

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Section: Theoretical Background 21 Envelope Analysis and Its Implemementioning

confidence: 99%

Efficient implementation of envelope analysis on resources limited wireless sensor nodes for accurate bearing fault diagnosis

Feng

Zhao

et al. 2017

Measurement

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“…In practical application like rotating machine fault diagnosis, there is another widely existing signals called unilateral attenuation impulse [ 28 , 29 , 30 , 31 , 32 , 33 ]. Rotating machinery response is often characterized by the presence of periodic impulses modulated by high-frequency harmonic components.…”

Section: System Performance Of Upsrmentioning

confidence: 99%

“…Figure 7 b shows its envelope signal calculated by Hilbert transform (HT) which is unilateral with SNR of −14.36 dB according to Equation (22). In order to have the simulated impulsive signal, it is generated according to the equation [ 32 , 33 ] where the initial phase is ignored:

…”

Section: System Performance Of Upsrmentioning

confidence: 99%

Stochastic Resonance in an Underdamped System with Pinning Potential for Weak Signal Detection

Zhang

Kong

2015

Sensors

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Stochastic resonance (SR) has been proved to be an effective approach for weak sensor signal detection. This study presents a new weak signal detection method based on a SR in an underdamped system, which consists of a pinning potential model. The model was firstly discovered from magnetic domain wall (DW) in ferromagnetic strips. We analyze the principle of the proposed underdamped pinning SR (UPSR) system, the detailed numerical simulation and system performance. We also propose the strategy of selecting the proper damping factor and other system parameters to match a weak signal, input noise and to generate the highest output signal-to-noise ratio (SNR). Finally, we have verified its effectiveness with both simulated and experimental input signals. Results indicate that the UPSR performs better in weak signal detection than the conventional SR (CSR) with merits of higher output SNR, better anti-noise and frequency response capability. Besides, the system can be designed accurately and efficiently owing to the sensibility of parameters and potential diversity. The features also weaken the limitation of small parameters on SR system.

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“…The output jumps between two equilibrium points (x = ± √ a) when the potential function (U (x) = − 1 2 ax 2 + 1 4 x 4 ) fluctuates at the barrier height (∆U = a 2 /4), which is called stochastic resonance. 2,3,5,6,8,9 A weak signal is more easily detected because noise energy is transferred from N(t, σ) to S(t). There are two requirements for the system to work in a state of stochastic resonance: (1) A must be close to but not (2) σ must be high enough to drive a jump but not be too high.…”

Section: A Introduction To Stochastic Resonancementioning

confidence: 99%

“…[3][4][5] Parameter setting is one of the most critical issues in the use of stochastic resonance. 2,[6][7][8][9] Step length h is adaptively adjusted employing the step-changed stochastic resonance method proposed by Li.…”

Section: Introductionmentioning

confidence: 99%

Parameter-induced stochastic resonance based on spectral entropy and its application to weak signal detection

Zhang

2015

Review of Scientific Instruments

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The parameter-induced stochastic resonance based on spectral entropy (PSRSE) method is introduced for the detection of a very weak signal in the presence of strong noise. The effect of stochastic resonance on the detection is optimized using parameters obtained in spectral entropy analysis. Upon processing employing the PSRSE method, the amplitude of the weak signal is enhanced and the noise power is reduced, so that the frequency of the signal can be estimated with greater precision through spectral analysis. While the improvement in the signal-to-noise ratio is similar to that obtained using the Duffing oscillator algorithm, the computational cost reduces from O(N(2)) to O(N). The PSRSE approach is applied to the frequency measurement of a weak signal made by a vortex flow meter. The results are compared with those obtained applying the Duffing oscillator algorithm.

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The Recovery of Weak Impulsive Signals Based on Stochastic Resonance and Moving Least Squares Fitting

Cited by 10 publications

References 13 publications

Efficient implementation of envelope analysis on resources limited wireless sensor nodes for accurate bearing fault diagnosis

Efficient implementation of envelope analysis on resources limited wireless sensor nodes for accurate bearing fault diagnosis

Stochastic Resonance in an Underdamped System with Pinning Potential for Weak Signal Detection

Parameter-induced stochastic resonance based on spectral entropy and its application to weak signal detection

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