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

High-Performance Adaptive Weak Fault Diagnosis Based on the Global Parameter Optimization Model of a Cascaded Stochastic Resonance System

Stochastic resonance (SR), as a type of noise-assisted signal processing method, has been widely applied in weak signal detection and mechanical weak fault diagnosis. In order to further improve the weak signal detection performance of SR-based approaches and realize high-performance weak fault diagnosis, a global parameter optimization (GPO) model of a cascaded SR system is proposed in this work. The cascaded SR systems, which involve multiple multi-parameter-adjusting SR systems with both bistable and tri-stable potential functions, are first introduced. The fixed-parameter optimization (FPO) model and the GPO models of the cascaded systems to achieve optimal SR outputs are proposed based on the particle swarm optimization (PSO) algorithm. Simulated results show that the GPO model is capable of achieving a better SR output compared to the FPO model with rather good robustness and stability in detecting low signal-to-noise ratio (SNR) weak signals, and the tri-stable cascaded SR system has a better weak signal detection performance compared to the bistable cascaded SR system. Furthermore, the weak fault diagnosis approach based on the GPO model of the tri-stable cascaded system is proposed, and two rolling bearing weak fault diagnosis experiments are performed, thus verifying the effectiveness of the proposed approach in high-performance adaptive weak fault diagnosis.

A Stochastic Resonance Electrocardiogram Enhancement Algorithm for Robust QRS Detection

This study presents a new QRS detection algorithm making use of the background noise that is inevitably present in electrocardiogram (ECG) recordings. The algorithm suppresses noise, enhances the QRS-waves, and applies a threshold for QRS detection. Noise suppression and QRS enhancement are performed by a band-pass filter stage followed by a nonlinear stage based on the interaction of a particle inside an underdamped monostable potential well. The nonlinear stage maximizes the output when there is a QRS-wave and minimizes the output otherwise. One of the instruments that the nonlinear stage uses to enhance the QRS-waves is stochastic resonance, where the output is maximized for a non-zero intensity background noise. In terms of QRS-wave detection F1 score, which ranges from 98.87% to 99.99% on four major benchmarking databases (MIT-BIH Arrhythmia, QT, European ST-T, and MIT-BIH Noise Stress Test), the algorithm outperforms all existing ECG processing algorithms. The study, for the first time, demonstrates QRS-enhancement by facilitating stochastic resonance while suppressing in-band noise of ECG signals. Detecting QRS-waves as the ECG data streams, having a complexity of O(n), and not requiring any training data make the algorithm convenient for real-time ECG monitoring applications with limited computational resources.

Optimal Self-Induced Stochastic Resonance in Multiplex Neural Networks: Electrical vs. Chemical Synapses

Electrical and chemical synapses shape the dynamics of neural networks, and their functional roles in information processing have been a longstanding question in neurobiology. In this paper, we investigate the role of synapses on the optimization of the phenomenon of self-induced stochastic resonance in a delayed multiplex neural network by using analytical and numerical methods. We consider a two-layer multiplex network in which, at the intra-layer level, neurons are coupled either by electrical synapses or by inhibitory chemical synapses. For each isolated layer, computations indicate that weaker electrical and chemical synaptic couplings are better optimizers of self-induced stochastic resonance. In addition, regardless of the synaptic strengths, shorter electrical synaptic delays are found to be better optimizers of the phenomenon than shorter chemical synaptic delays, while longer chemical synaptic delays are better optimizers than longer electrical synaptic delays; in both cases, the poorer optimizers are, in fact, worst. It is found that electrical, inhibitory, or excitatory chemical multiplexing of the two layers having only electrical synapses at the intra-layer levels can each optimize the phenomenon. Additionally, only excitatory chemical multiplexing of the two layers having only inhibitory chemical synapses at the intra-layer levels can optimize the phenomenon. These results may guide experiments aimed at establishing or confirming to the mechanism of self-induced stochastic resonance in networks of artificial neural circuits as well as in real biological neural networks.

Extraction of Bridge Fundamental Frequencies Utilizing a Smartphone MEMS Accelerometer

Smartphone MEMS (Micro Electrical Mechanical System) accelerometers have relatively low sensitivity and high output noise density. Therefore, it cannot be directly used to track feeble vibrations such as structural vibrations. This article proposes an effective increase in the sensitivity of the smartphone accelerometer utilizing the stochastic resonance (SR) phenomenon. SR is an approach where, counter-intuitively, feeble signals are amplified rather than overwhelmed by the addition of noise. This study introduces the 2D-frequency independent underdamped pinning stochastic resonance (2D-FI-UPSR) technique, which is a customized SR filter that enables identifying the frequencies of weak signals. To validate the feasibility of the proposed SR filter, an iPhone device is used to collect bridge acceleration data during normal traffic operation and the proposed 2D-FI-UPSR filter is used to process these data. The first four fundamental bridge frequencies are successfully identified from the iPhone data. In parallel to the iPhone, a highly sensitive wireless sensing network consists of 15 accelerometers (Silicon Designs accelerometers SDI-2012) is installed to validate the accuracy of the extracted frequencies. The measurement fidelity of the iPhone device is shown to be consistent with the wireless sensing network data with approximately 1% error in the first three bridge frequencies and 3% error in the fourth frequency.

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.

Early Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by Maximum Correlated Kurtosis Deconvolution

The early fault characteristics of rolling element bearings carried by vibration signals are quite weak because the signals are generally masked by heavy background noise. To extract the weak fault characteristics of bearings from the signals, an improved spectral kurtosis (SK) method is proposed based on maximum correlated kurtosis deconvolution (MCKD). The proposed method combines the ability of MCKD in indicating the periodic fault transients and the ability of SK in locating these transients in the frequency domain. A simulation signal overwhelmed by heavy noise is used to demonstrate the effectiveness of the proposed method. The results show that MCKD is beneficial to clarify the periodic impulse components of the bearing signals, and the method is able to detect the resonant frequency band of the signal and extract its fault characteristic frequency. Through analyzing actual vibration signals collected from wind turbines and hot strip rolling mills, we confirm that by using the proposed method, it is possible to extract fault characteristics and diagnose early faults of rolling element bearings. Based on the comparisons with the SK method, it is verified that the proposed method is more suitable to diagnose early faults of rolling element bearings.