Weak signal enhancement for machinery fault diagnosis based on a novel adaptive multi-parameter unsaturated stochastic resonance

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

doi:10.1016/j.apacoust.2021.108609

Cited by 30 publications

(4 citation statements)

References 39 publications

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“…Furthermore, the segmented structure of potential functions can enhance system performance. Shi et al [25] proposed a segmented multi-parameter stochastic resonance system, which linearizes the potential function at both ends to improve the output saturation issue of classical potential functions, thereby optimizing system output.…”

Section: Introductionmentioning

confidence: 99%

Delay segmented tristable stochastic resonance system driven by non-gaussian colored noise and its application in bearing fault detection

He,

Cao,

Zhang

2024

Phys. Scr.

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This study investigates the Delayed Segmented Tristable Stochastic Resonance (DSTSR) system under the influence of additive non-Gaussian colored noise. The research employs an improved segmented tristable potential function, wherein the equilibrium points and barrier heights can be independently controlled by parameters. Simultaneously, the segmented function on both sides reduces the restrictions of higher-order terms on the walls of the potential wells. The equivalent Langevin equation for the DSTSR system is obtained using the path integral method, the unified colored noise approximation method, and the small-delay approximation. Subsequently, the theoretical expressions for the steady-state probability density, mean first passage time (MFPT), and Signal-to-Noise Ratio (SNR) are derived from the resulting equations, and the impact of variations in system parameters on these performance metrics is discussed. Additionally, Monte Carlo simulations for MFPT are conducted to verify the accuracy of the theoretical derivations. Combining the results from the theoretical section and the impact of parameters on system performance, the article employs an adaptive genetic algorithm to optimize system parameters. This algorithm is then applied to simulation experiments and bearing fault detection. In the simulation experiments, the DSTSR system is compared with other systems. The results indicate that the DSTSR system exhibits the highest SNR improvement. Furthermore, in bearing fault detection under non-Gaussian colored noise, the DSTSR system shows higher spectral amplitude and SNR at the fault frequency compared to the tristable stochastic resonance system and the segmented tristable stochastic resonance system without time delay feedback. This suggests that stochastic resonance can effectively detect weak signals in non-Gaussian non-white noise scenarios, and the introduction of time delay contributes to the occurrence of stochastic resonance to a certain extent.

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Section: Introductionmentioning

confidence: 99%

Delay segmented tristable stochastic resonance system driven by non-gaussian colored noise and its application in bearing fault detection

He,

Cao,

Zhang

2024

Phys. Scr.

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“…The linear response theory is then developed by Gammaitoni et al [13], and the theory of residence time distribution is later introduced by Zhou and Moss [14]. By then, the three major theories which lay the classical theoretical framework of SR systems are formally proposed, and they are successfully applied in the fields of mechanical fault diagnosis [15][16][17][18], electromagnetism and communication [19][20][21], image processing [22][23][24] and medical signal analyses [25][26][27][28], etc.…”

Section: Introductionmentioning

confidence: 99%

Linearly-coupled sigmoid bistable stochastic resonance for weak signal detection

Zong,

An,

Zhang

et al. 2024

Meas. Sci. Technol.

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The paper focuses on developing a stochastic resonance system designed for the detection of weak signals under Alpha-stable-distributed noises. Initially, in view of the strong impulsive characteristics of noises, a Linearly-coupled Sigmoid Bistable Stochastic Resonance (LSBSR) system is proposed, which is constructed by potential function and sigmoid function. Through formula derivation, it is theoretically proved that the output SNR of the LSBSR system is superior to that of the classical bistable stochastic resonance (CBSR) system. Then, a new signal processing strategy based on the LSBSR system is introduced. Simulation experiments have demonstrated that under the input SNR=-20dB, the detection probability of the LSBSR system exceeds 95% for the Alpha-stable-distributed noise with α=1.5. When α is reduced to 0.1, the detection probability approaches 80%, significantly outperforming other detection methods. Finally, the LSBSR system is applied to detect sea-trial signals with an SNR improvement (SNRI) of 22.5dB, which further validates the practicability of the proposed system.

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“…López et al [13] developed an adaptive SR method on hidden Markov model (HMM) to detect the faulty signal autonomously. Shi et al [14] employed a adaptive multi-parameter unsaturation bistable SR system to realize feature frequency detection in machinery fault signals. Mba et al [15] built a gearbox condition monitoring scheme using SR and the HMM.…”

Section: Introductionmentioning

confidence: 99%

Adaptive progressive learning stochastic resonance for weak signal detection

zong¹,

Men²,

An³

et al. 2023

Meas. Sci. Technol.

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Stochastic resonance (SR) can enhance signals by using noise. This has attracted more attention in the field of weak signal detection. In practical applications, owing to the non-adjustability of noisy signals, SR is required to adjust the system parameters adaptively to satisfy the conditions of the SR phenomenon. In this paper, an adaptive progressive learning stochastic resonance (APLSR) method is proposed to improve the detection ability of weak signal, and the SR phenomenon is quantitatively defined. A theoretical learning framework is established with an improved reinforcement learning model by mapping the nonlinear system parameter space to a progressive learning set. By selecting a proper learning layer within a determined constraint range, the matching system parameters can be quickly and accurately searched to generate a desired optimal output. Numerical simulation results show that the signal energy and the output-SNR can be enhanced significantly, which reflects an excellent weak signal detection performance especially for low signal-to-noise ratio (SNR) conditions. Finally, a diagnosis on the outer race fault signals of rolling bearing confirms that the proposed method can effectively detect fault characteristics.

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Weak signal enhancement for machinery fault diagnosis based on a novel adaptive multi-parameter unsaturated stochastic resonance

Cited by 30 publications

References 39 publications

Delay segmented tristable stochastic resonance system driven by non-gaussian colored noise and its application in bearing fault detection

Delay segmented tristable stochastic resonance system driven by non-gaussian colored noise and its application in bearing fault detection

Linearly-coupled sigmoid bistable stochastic resonance for weak signal detection

Adaptive progressive learning stochastic resonance for weak signal detection

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