Stochastic resonance in a high-dimensional space coupled bistable system and its application

Li, Mengdi; Shi, Peiming; Zhang, Wenyue; Han, Dongying

doi:10.1016/j.apm.2022.08.023

Cited by 18 publications

(6 citation statements)

References 43 publications

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“…Liu et al [30] explored the SR dynamics of the N-coupled systems within a periodic sinusoidal potential, along with investigating the influence of parameter variations on the occurrence of the SR in both linear and nonlinear couplings. Li et al [31] put forward a highdimensional space model based on a coupled bistable SR system, wherein the analysis of the impact of system parameters and dimensions on the system output is conducted. Despite the extensive theoretical investigations conducted on coupled periodic SR systems, the potential advantages of leveraging noise to amplify weak useful signals in three-dimensional coupled periodic SR systems have not been fully explored.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of rolling bearing based on the three-dimensional coupled periodic potential-induced stochastic resonance

Xia,

Lei,

et al. 2024

Meas. Sci. Technol.

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Conventional bistable and monostable stochastic resonance (SR) methods exhibit certain limitations in their capacity to enhance and extract incipient characteristics. Firstly, the inherent potential function structure, characterized by a singular stable-state paradigm, proves inadequate in accommodating the heterogeneous and multifaceted condition monitoring signals. Secondly, the interconnected dynamic characteristics of the mechanical signals remain unaccounted for. Furthermore, conventional SR methods persist in utilizing a fixed constant as the critical system parameter, thereby neglecting the synergistic interaction among monitoring signals, potential function structures, and scale factors. Owing to the rich dynamic characteristics of the three-dimensional multi-stable coupled periodic potential SR system, it demonstrates superior noise utilization compared to monostable and bistable systems. In view of this, the present formulates a three-dimensional spatial model employing a coupled periodic potential model with nonlinear coupling. Subsequently, a pioneering method for diagnosing rolling bearing faults is introduced, utilizing the framework of three-dimensional multi-stable coupled periodic potential-induced SR. Simulation and experimental results illustrate that this approach effectively enhances and extracts the subtle fault characteristics of rolling bearings, ensuring a clear distinction between the spectral peak at the bearing fault characteristic frequency and the spectral peak originating from the interference noise.

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Section: Introductionmentioning

confidence: 99%

Fault diagnosis of rolling bearing based on the three-dimensional coupled periodic potential-induced stochastic resonance

Xia,

Lei,

et al. 2024

Meas. Sci. Technol.

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“…Duan et al [31] studied the SR in an uncoupled parallel array of overdamped bistable nonlinear system and found that when the size of array goes to infinity the maximum SNR gain of a signal would be obtained. Li et al [32] constructed and further explored a high-dimensional space coupled bistable system for signal detection. Meanwhile, Xu et al [33] studied the SR in high-order degradation bistable systems for machine fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Fourth-stable stochastic resonance and its application to weak fault signal detection of rotating machinery

Yun,

Zhang,

Shang

et al. 2024

Phys. Scr.

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Weak characteristic extraction is vital for weak fault signal detection of machinery. Stochastic resonance (SR) is able to transfer noise energy into weak fault characteristic frequency excited by a defect of machines. However, the potential function in SR is vital to enhance weak fault characteristic frequency and determines the capability of SR to improve the signal-to-noise ratio (SNR) of a noisy signal. Now, common potential functions include monostable, bistable and even tri-stable potentials but fourth-stable SR has not been studied and applied to detect early fault characteristic frequency. In this paper, thus, we would investigate the behaviors of SR with a fourth-stable potential subject to additive noise, in which the approximate theoretical expression of the power done by SR is derived to demonstrate the fourth-stable SR. Then, a SR method with the fourth-stable potential is proposed to enhance weak fault characteristic frequency, in which these system parameters are adjusted by using SNR as the objective function and using genetic algorithms adaptively. In this paper, thus, Finally, the proposed method is verified by using a simulated signal with noise and two early fault experiment of rolling element bearings with different levels of defects on the outer and inner races. Moreover, the proposed method is compared with wavelet denoising and fast kurtogram methods. The comparisons indicate that the proposed method has the better performance for enhancing weak fault characteristic frequency or weak useful signals than other two methods and is available to weak fault signal detection of machinery.

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“…Numerous scholars have proposed Empirical Modal Decomposition (EMD) [4], Stochastic Resonance (SR) [5,6], Wavelet Packet Decomposition (WPD) [7,8], and other techniques for diagnosing the characteristics of bearing fault signals and achieved positive results. However, the above-mentioned techniques are based on the assumption of a linear system, and the analysis process is not adaptive, so they cannot accurately describe the inherent characteristics of the signal.…”

Section: Introductionmentioning

confidence: 99%

Approach to the Quantitative Diagnosis of Rolling Bearings Based on Optimized VMD and Lempel–Ziv Complexity under Varying Conditions

Wang

Yang

Han

et al. 2023

Sensors

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The quantitative diagnosis of rolling bearings is essential to automating maintenance decisions. Over recent years, Lempel–Ziv complexity (LZC) has been widely used for the quantitative assessment of mechanical failures as one of the most valuable indicators for detecting dynamic changes in nonlinear signals. However, LZC focuses on the binary conversion of 0–1 code, which can easily lose some effective information about the time series and cannot fully mine the fault characteristics. Additionally, the immunity of LZC to noise cannot be insured, and it is difficult to quantitatively characterize the fault signal under strong background noise. To overcome these limitations, a quantitative bearing fault diagnosis method based on the optimized Variational Modal Decomposition Lempel–Ziv complexity (VMD-LZC) was developed to fully extract the vibration characteristics and to quantitatively characterize the bearing faults under variable operating conditions. First, to compensate for the deficiency that the main parameters of the variational modal decomposition (VMD) have to be selected by human experience, a genetic algorithm (GA) is used to optimize the parameters of the VMD and adaptively determine the optimal parameters [k, α] of the bearing fault signal. Furthermore, the IMF components that contain the maximum fault information are selected for signal reconstruction based on the Kurtosis theory. The Lempel–Ziv index of the reconstructed signal is calculated and then weighted and summed to obtain the Lempel–Ziv composite index. The experimental results show that the proposed method is of high application value for the quantitative assessment and classification of bearing faults in turbine rolling bearings under various operating conditions such as mild and severe crack faults and variable loads.

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Stochastic resonance in a high-dimensional space coupled bistable system and its application

Cited by 18 publications

References 43 publications

Fault diagnosis of rolling bearing based on the three-dimensional coupled periodic potential-induced stochastic resonance

Fault diagnosis of rolling bearing based on the three-dimensional coupled periodic potential-induced stochastic resonance

Fourth-stable stochastic resonance and its application to weak fault signal detection of rotating machinery

Approach to the Quantitative Diagnosis of Rolling Bearings Based on Optimized VMD and Lempel–Ziv Complexity under Varying Conditions

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