The characteristic analysis of stochastic resonance and bearing fault diagnosis based on NWSG model driven by trichotomous noise

Zhang, Gang; Yang, Yulei; Zhang, Tianqi

doi:10.1016/j.cjph.2019.05.005

Cited by 18 publications

(3 citation statements)

References 27 publications

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“…e model proposed in this paper has four parameters, namely k 1 , k 2 , k 3 , r and the dimension of the genetic algorithm is set as four dimensions [11][12][13].…”

Section: Adaptive Genetic Algorithmmentioning

confidence: 99%

Analysis of Asymmetric Piecewise Linear Stochastic Resonance Signal Processing Model Based on Genetic Algorithm

Jiang

2020

Complexity

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The stochastic resonance system has the advantage of making the noise energy transfer to the signal energy. Because the existing stochastic resonance system model has the problem of poor performance, an asymmetric piecewise linear stochastic resonance system model is proposed, and the parameters of the model are optimized by a genetic algorithm. The signal-to-noise ratio formula of the model is derived and analyzed, and the theoretical basis for better performance of the model is given. The influence of the asymmetric coefficient on system performance is studied, which provides guidance for the selection of initial optimization range when a genetic algorithm is used. At the same time, the formula is verified and analyzed by numerical simulation, and the correctness of the formula is proved. Finally, the model is applied to bearing fault detection, and an adaptive genetic algorithm is used to optimize the parameters of the system. The results show that the model has an excellent detection effect, which proves that the model has great potential in fault detection.

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“…e model proposed in this paper has four parameters, namely k 1 , k 2 , k 3 , r and the dimension of the genetic algorithm is set as four dimensions [11][12][13].…”

Section: Adaptive Genetic Algorithmmentioning

confidence: 99%

Analysis of Asymmetric Piecewise Linear Stochastic Resonance Signal Processing Model Based on Genetic Algorithm

Jiang

2020

Complexity

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“…Following the discussion on equation (1) regarding the potential effect of wall slope on particle displacement, we also investigate the response in a monostable well having steeper slopes around the stable point, namely Woods-Saxon (WS) well [15].…”

Section: Steep-slope Monostable (Stm) Well (Figure 2(b))mentioning

confidence: 99%

Investigating well potential parameters on neural spike enhancement in a stochastic-resonance pre-emphasis algorithm

2021

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Objective. Background noise experienced during extracellular neural recording limits the number of spikes that can be reliably detected, which ultimately limits the performance of next-generation neuroscientific work. In this study, we aim to utilize stochastic resonance (SR), a technique that can help identify weak signals in noisy environments, to enhance spike detectability. Approach. Previously, an SR-based pre-emphasis algorithm was proposed, where a particle inside a 1D potential well is exerted by a force defined by the extracellular recording, and the output is obtained as the displacement of the particle. In this study, we investigate how the well shape and damping status impact the output signal-to-noise ratio (SNR). We compare the overdamped and underdamped solutions of shallow- and steep-wall monostable wells and bistable wells in terms of SNR improvement using two synthetic datasets. Then, we assess the spike detection performance when thresholding is applied on the output of the well shape-damping status configuration giving the best SNR enhancement. Main results. The SNR depends on the well-shape and damping-status type as well as the input noise level. The underdamped solution of the shallow-wall monostable well can yield to more than four orders of magnitude greater SNR improvement compared to other configurations for low noise intensities. Using this configuration also results in better spike detection sensitivity and positive predictivity than the state-of-the-art spike detection algorithms for a public synthetic dataset. For larger noise intensities, the overdamped solution of the steep-wall monostable well provides better spike enhancement than the others. Significance. The dependence of SNR improvement on the input signal noise level can be used to design a detector with multiple outputs, each more sensitive to a certain distance from the electrode. Such a detector can potentially enhance the performance of a successive spike sorting stage.

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“…In recent years, the SR method has been widely applied in fault feature extraction and recognition of bearings [22,23]. Li and Shi [24] introduced a new piecewise nonlinear SR to enhance early fault features of machinery.…”

Section: Introductionmentioning

confidence: 99%

Weak Fault Detection for Rolling Bearings in Varying Working Conditions through the Second‐Order Stochastic Resonance Method with Barrier Height Optimization

Shi

Zhou

et al. 2021

Shock and Vibration

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The stochastic resonance (SR) method is widely applied to fault feature extraction of rotary machines, which is capable of improving the weak fault detection performance by energy transformation through the potential well function. The potential well functions are mostly set fixed to reduce computational complexity, and the SR methods with fixed potential well parameters have better performances in stable working conditions. When the fault frequency changes in variable working conditions, the signal processing effect becomes different with fixed parameters, leading to errors in fault detection. In this paper, an underdamped second-order adaptive general variable-scale stochastic resonance (USAGVSR) method with potential well parameters’ optimization is put forward. For input signals with different fault frequencies, the potential well parameters related to the barrier height are figured out and optimized through the ant colony algorithm. On this basis, further optimization is carried out on undamped factor and step size for better fault detection performance. Cases with diverse fault types and in different working conditions are studied, and the performance of the proposed method is validated through experiments. The results testify that this method has better performances of weak fault feature extraction and can accurately identify different fault types in the input signals. The method proves to be effective in the weak fault extraction and classification and has a good application prospect in rolling bearings’ fault feature recognition.

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The characteristic analysis of stochastic resonance and bearing fault diagnosis based on NWSG model driven by trichotomous noise

Cited by 18 publications

References 27 publications

Analysis of Asymmetric Piecewise Linear Stochastic Resonance Signal Processing Model Based on Genetic Algorithm

Analysis of Asymmetric Piecewise Linear Stochastic Resonance Signal Processing Model Based on Genetic Algorithm

Investigating well potential parameters on neural spike enhancement in a stochastic-resonance pre-emphasis algorithm

Weak Fault Detection for Rolling Bearings in Varying Working Conditions through the Second‐Order Stochastic Resonance Method with Barrier Height Optimization

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