Note: signal amplification and filtering with a tristable stochastic resonance cantilever

Enhanced fault diagnosis via stochastic resonance in a piecewise asymmetric bistable system

Weak fault signals are often overwhelmed by strong noise or interference. The key issue in fault diagnosis is to accurately extract useful fault characteristics. Stochastic resonance is an important signal processing method that utilizes noise to enhance weak signals. In this paper, to address the issues of output saturation and imperfect optimization of potential structure models in classical bistable stochastic resonance (CBSR), we propose a piecewise asymmetric stochastic resonance system. A two-state model is used to theoretically derive the output signal-to-noise ratio (SNR) of the bistable system under harmonic excitations, which is compared with the SNR of CBSR to demonstrate the superiority of the method. The method is then applied to fault data. The results indicate that it can achieve a higher output SNR and higher spectral peaks at fault characteristic frequencies/orders, regardless of whether the system operates under fixed or time-varying speed conditions. This study provides new ideas and theoretical guidance for improving the accuracy and reliability of fault diagnosis technology.

Nonlinear resonance decomposition for weak signal detection

This paper attempts to investigate the behaviors of coupling stochastic resonance (CSR) subject to α-stable noise and a periodic signal by using the residence-time ratio. Then, a nonlinear resonance decomposition is designed to successfully enhance and detect weak unknown multi-frequency signals embedded in strong α-stable noise by decomposing the noisy signal into a series of useful resonant components and a residue, where the residence-time ratio, instead of the output signal-to-noise ratio and other objective functions depending on the prior knowledge of the signals to be detected, can optimize the CSR to enhance weak unknown signals. Finally, the nonlinear resonance decomposition is used to process the raw vibration signal of rotating machinery. It is found that the nonlinear resonance decomposition is able to decompose the weak characteristic signal and its harmonics, identifying the imbalance fault of the rotor. Even the proposed method is superior to the empirical mode decomposition method in this experiment. This research is helpful to design the noise enhanced signal decomposition techniques by harvesting the energy of noise to enhance and decompose the useful resonant components from a nonstationary and nonlinear signal.

Stochastic resonance in a single-well potential and its application in rolling bearing fault diagnosis

In this paper, a single-well model based on the piecewise function and classical bistable stochastic resonance (CBSR) is proposed. The steady state probability density of particles and mean first passage time in the model are calculated. The output characteristics and performance of the proposed model are analyzed through numerical simulation. On the basis of CBSR and the proposed model, an adaptive system is established (ACSSR) to generate the highest gain of signal-to-noise ratio (SNR_g). Finally, the effectiveness of ACSSR in weak signal detection is verified with both simulated and experimental input signals. The results indicate that the ACSSR could detect the defect signal correctly and improve the SNR_g.

Drive-By Bridge Frequency Identification under Operational Roadway Speeds Employing Frequency Independent Underdamped Pinning Stochastic Resonance (FI-UPSR)

Recently, drive-by bridge inspection has attracted increasing attention in the bridge monitoring field. A number of studies have given confidence in the feasibility of the approach to detect, quantify, and localize damages. However, the speed of the inspection truck represents a major obstacle to the success of this method. High speeds are essential to induce a significant amount of kinetic energy to stimulate the bridge modes of vibration. On the other hand, low speeds are necessary to collect more data and to attenuate the vibration of the vehicle due to the roughness of the road and, hence, magnify the bridge influence on the vehicle responses. This article introduces Frequency Independent Underdamped Pinning Stochastic Resonance (FI-UPSR) as a new technique, which possesses the ability to extract bridge dynamic properties from the responses of a vehicle that passes over the bridge at high speed. Stochastic Resonance (SR) is a phenomenon where feeble information such as weak signals can be amplified through the assistance of background noise. In this study, bridge vibrations that are present in the vehicle responses when it passes over the bridge are the feeble information while the noise counts for the effect of the road roughness on the vehicle vibration. UPSR is one of the SR models that has been chosen in this study for its suitability to extract the bridge vibration. The main contributions of this article are: (1) introducing a Frequency Independent-Stochastic Resonance model known as the FI-UPSR and (2) implementing this model to extract the bridge vibration from the responses of a fast passing vehicle.

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

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.

Note: On-line weak signal detection via adaptive stochastic resonance

We design an instrument with a novel embedded adaptive stochastic resonance (SR) algorithm that consists of a SR module and a digital zero crossing detection module for on-line weak signal detection in digital signal processing applications. The two modules are responsible for noise filtering and adaptive parameter configuration, respectively. The on-line weak signal detection can be stably achieved in seconds. The prototype instrument exhibits an advance of 20 dB averaged signal-to-noise ratio and 5 times averaged adjust R-square as compared to the input noisy signal, in considering different driving frequencies and noise levels.