Weak Signal Extraction Research Articles

Pulse Wave Measuring via Fingertip-Like Sensor to Analyze and Identify Pulse-Rate Status

Pulse wave contains detailed information about the human cardiovascular system and is associated with various physiological changes. A fingertip-like pulse sensor based on a piezoresistive sensor was constructed to extract weak pulse signals with lossless performance. In the sensing mechanism, the external force was deformed through the flexible film and transmitted to the Wheatstone bridge to realize the force–electric conversion measurement. The sensor experiments revealed that the sensor’s performance had excellent linearity ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R}^{2}&gt;0.9999$ </tex-math></inline-formula> ), low hysteresis (<1%), and high sensitivity (0.04 V/kPa). A pulse acquisition device based on pulse measuring was developed and applied to clinical practice for pulse signal measurement. Genuine pulse signals were obtained by removing high-frequency noise and baseline drift from the original pulse signal. A total of 55 volunteers aged 20–85 years with different health conditions were recruited, and eight were previously diagnosed with arrhythmia. Results demonstrated that the pulse period (PP) interval presented three different distribution patterns. Pulse signals obtained at different locations with various pressures showed potential application in digital pulse diagnostics.

High-Performance Digital Lock-In Amplifier Module Based on an Open-Source Red Pitaya Platform: Implementation and Applications

A lock-in amplifier (LIA) is an instrument that can extract weak signals from extremely noisy environments, and it is widely used in various precision measurement fields. However, there are still challenges in lock-in techniques in experiments that require high-frequency or multiple lock-in channels, such as high-frequency vibration detection and experiments with quantum sensors. The direct use of commercial LIAs will result in high experimental costs. Herein, a high-performance digital LIA module based on an open-source Red Pitaya (RP) platform is constructed. The quantitative description of the demodulation results, efficient data transfer method, and the development of the automated measurement functions in experiments are carried out in this module. As demonstrations, the phase difference and thermal noise measurements are conducted using the module. The research on temperature sensing with the frequency shift of quartz crystals and semiconductor doping concentration measurements has also been implemented. These demonstrations show that the digital lock-in module has good application prospects.

Synchronous capture method of multi-channel weak signal in long-distance communication network

In order to improve the accuracy, efficiency and network throughput of multi-channel weak signal synchronous acquisition in the network, a multi-channel weak signal synchronous acquisition method in remote communication network is designed. Firstly, by analyzing the multi-channel structure of remote communication network, the interference factors of multi-channel weak signal acquisition are determined. The parameter model method is used to estimate the bispectrum of weak signals, complete the multi-channel weak signal extraction of remote communication network, and preprocess the multi-channel weak signals of remote communication network by average filtering method. On this basis, the characteristics of multi-channel weak signals in the remote communication network are judged, and their characteristics are changed through the short time window function in the time domain, and the multi-channel weak signal synchronous catcher in the remote communication network is constructed to realize the synchronous acquisition of multi-channel weak signals in the remote communication network. The experimental results show that this method has high accuracy, short time-consuming and good network throughput. The acquisition accuracy of this method is always maintained at more than 90%.

A Weak SNR Signal Extraction Method for Near-Bit Attitude Parameters Based on DWT

In practice, the near-bit signals are usually accompanied by noises generated by drilling tool vibration and high-speed rotary. This study introduces discrete wavelet transform to a well drilling area and presents a new weal signal extraction algorithm, which can eliminate the vibration and rotary noises effectively and obtain the useful gravity attitude signals based on the discrete wavelet transform through a hard threshold method. The effectiveness and usefulness of our method are verified via Matlab simulation and actual drilling data.

A Fast Sparse Decomposition Based on the Teager Energy Operator in Extraction of Weak Fault Signals.

In order to diagnose an incipient fault in rotating machinery under complicated conditions, a fast sparse decomposition based on the Teager energy operator (TEO) is proposed in this paper. In this proposed method, firstly, the TEO is employed to enhance the envelope of the impulses, which is more sensitive to frequency and can eliminate the low-frequency harmonic component and noise; secondly, a smoothing filtering algorithm was adopted to suppress the noise in the TEO envelope; thirdly, the fault signal was reconstructed by multiplication of the filtered TEO envelope and the original fault signal; finally, sparse decomposition was used based on a generalized S-transform (GST) to obtain the sparse representation of the signal. The proposed preprocessing method using the filtered TEO can overcome the interference of high-frequency noise while maintaining the structure of fault impulses, which helps the processed signal perform better on sparse decomposition; sparse decomposition based on GST was used to represent the fault signal more quickly and more accurately. Simulation and application prove that the proposed method has good accuracy and efficiency, especially in conditions of very low SNR, such as impulses with anSNR of −8.75 dB that are submerged by noise of the same amplitude.

A novel two-dimensional exponential potential bi-stable stochastic resonance system and its application in bearing fault diagnosis

It is difficult for classical stochastic resonance systems to extract weak signals under strong noise environment, therefore a novel two-dimensional exponential potential bi-stable stochastic resonance system (NTBSR) is proposed. First, the equivalent potential function, the mean first-pass time (MFPT) and the output signal-to-noise ratio (SNR) of NTBSR are derived under the adiabatic approximation theory. At the same time, the influence of different system parameters on them is explored. Then, NTBSR, the one-dimensional bi-stable stochastic resonance system (OBSR) and the two-dimensional classical bi-stable stochastic resonance system (TCBSR) are respectively simulated numerically, based on the fourth-order Runge–Kutta algorithm. It is found that the output SNR of NTBSR is the best. Finally, the NTBSR is applied to the fault signal diagnosis of different types of bearings, and the parameters are optimized through the adaptive genetic algorithm (AGA). The test results are compared with wavelet transform method, and TCBSR. The detection results on two sets of bearing fault data show that the NTBSR system has better effects on the enhancement and detection of bearing fault signals, and it is verified that the stochastic resonance method is superior to the traditional wavelet transform method in terms of signal detection and noise utilization. This provides good theoretical support and application value for practical engineering application.

Research and Application of a Novel Combined Asymmetric Tristable Stochastic Resonance System

Aiming at the deficiency of weak signal amplification ability in traditional bistable system and combining with the advantages of tristable system and asymmetric system, three novel of combined asymmetric tristable stochastic resonance (SR) systems are proposed by combining the classical bistable potential model with the Gaussian potential model in this paper, namely, asymmetric well depth stochastic resonance (AWDSR), asymmetric well width stochastic resonance (AWWSR) and asymmetric well depth width stochastic resonance (AWDWSR). The stochastic resonance phenomena of the above three systems under [Formula: see text] noise with the influence of parameters are analyzed, respectively. Through empirical mode decomposition and adaptive genetic algorithm to optimize the parameters for weak signal extraction, it can be found that this method can achieve stronger SR performance. After the bearing fault diagnosis of the proposed systems, the detection results of the three asymmetric systems are obviously better than the classical tristable stochastic resonance (CTSR) and symmetric stochastic resonance (SSR), and the performance advantage of AWDWSR is the greatest. This can provide some reference value for practical engineering application.

A Piecewise Nonlinear Tri-Stable Stochastic Resonance System and its Application in Bearing Fault Diagnosis

An improved Piecewise Tri-stable Stochastic Resonance (PTSR) system is investigated to enhance the low performance of Classical Tri-stable Stochastic Resonance (CTSR) system for weak signal extraction. Firstly, the proposed PTSR system is theoretically compared with CTSR system. Secondly, based on adiabatic approximation theory, the analytical expressions for the Mean First Pass Time (MFPT) and the output Signal-to-Noise Ratio (SNR) of the system are derived. Afterwards, a PTSR-EMD system that combines PTSR system with empirical mode decomposition (EMD) is proposed. Finally, the above three systems are applied to the detection of harmonic signals and actual fault signals, and the superior performance of PTSR-EMD system over PTSR system and CTSR system are confirmed.

Review on Intelligent Diagnosis Technology of Electronically Controlled Fuel Injection System of ME Diesel Engine

ME diesel engine plays an important role in realizing energy conservation and environmental protection and improving the intelligent level of ship engine room. Because of its high technical content and high added value, using intelligent diagnosis technology to ensure safe and reliable work is an important means to realize intelligent engine room. Taking the ship electronic fuel injection system as an example, this paper introduces its working principle and common faults, analyzes and summarizes the research status of intelligent diagnosis technology at home and abroad in four aspects: fault mechanism, data measurement and feature extraction, fault mode classification and residual life prediction. Then, it analyzes the problems and future development trend of intelligent diagnosis technology, and points out that developing a health management system integrating weak fault signal extraction, multi-source data analysis, quantitative judgment of fault mode, remaining life prediction and maintenance suggestions is an important development goal in the future.

Research on the Weak Signal Extraction Method with Adaptive Stochastic Resonance Based on the Time Grid Sensor for PMSM

Because the output signal of the time grid sensor is so weak and is easily affected by the disturbing noise, it is difficult to extract the induction signal containing the position information, when the time grid sensor is used to detect the rotor position of the permanent magnet synchronous motor (PMSM). In this paper, an adaptive monostable stochastic resonance method is proposed to realize the weak signal detection of the time grid sensor. The tunnel magnetoresistance effect sensor (TMR) is adopted to detect the change of the rotor magnetic field of the motor in this method, and the detection model of the time grid sensor about PMSM is constructed. The monostable stochastic resonance theory is applied to extract the induction signal of the time grid sensor. As to the problem of the tuning the parameters of the monostable stochastic resonance system, the genetic algorithm is adopted to optimize the parameters of the monostable stochastic resonance system. The experimental results show that the monostable stochastic resonance method based on the genetic algorithm can effectively detect the weak induction signal of the time grid sensor about PMSM. This method solves the problem of the weak signal extraction of the time grid sensor for PMSM, and the detection accuracy is also improved.

High-Precision Electrical Impedance Tomography for Electrical Conductivity of Metallic Materials

Metal materials are subject to deformation, internal stress distribution, and cracking during processing, all of which affect the distribution of electrical conductivity of the metal. Suppose we can detect the conductivity distribution of metal materials in real time. In that case, we can complete the inverse imaging of metal material properties, structures, cracks, etc. and realize nondestructive flaw detection. However, metal materials' small resistance, high electrical conductivity, and susceptibility of voltage signals to noise signal interference make an accurate measurement of metal conductivity challenging. Therefore, this paper addresses the problem of detecting the conductivity distribution of metals by investigating a high-precision four-electrode AC measurement method. This technical approach combines laminar imaging techniques with high-precision weak signal extraction methods. On this basis, a method and equipment for high-precision electrical impedance tomography of metallic materials’ electrical conductivity were established. The way specifies a new number of electrodes and adopts a model of spaced excitation reference measurements. Single-frequency sinusoidal AC signal is used for excitation, and Shannon wavelet analysis is used for signal extraction and noise reduction. Super-resolution reconstruction algorithms are used for resistivity distribution image reconstruction to improve image quality. Based on the results of various comparative experiments, it is clear that this new functional technique method has good imaging stability and operability and can perform tasks such as analyzing the internal conductivity distribution of metals. This research provides an effective way of new ideas for the safe detection of metal structures, the changes in crystal tissue structure, and the study of metal properties. In particular, it expands the scope of research in the development and application of resistance tomography, which has tremendous commercial potential research significance.

Application of Improved Phase Lock Method in Weak Signal Extraction

The existing method of the weak signal under the background of strong noise (signal amplitude relative to the noise amplitude is much smaller) analysis is not very ideal. This study proposes an improved phase lock method to analyze the weak periodic signal under Gaussian white noise. Experimental results show that this method can better analyze weak signal-related information than the stochastic resonance method in a certain range of signal-to-noise ratio, and the amount of calculation is small, and the correlation theory is simple and adapted to the rapid detection of the signal; in order to further reduce the amount of calculation, the introduction of a high-order accumulation of the signal presence detection, experiments verify that the high-order accumulation can detect whether there is a weak signal in the Gaussian noise. The combination of high-order cumulant and improved phase lock method has a good effect on the analysis of weak periodic signals under strong noise backgrounds.

Research and application of a novel piecewise unsaturated asymmetric tristable stochastic resonance system

Early fault diagnosis of rolling bearings is of great significance in the application of mechanical equipment, which makes the extraction of weak fault signals particularly critical by stochastic resonance (SR). Compared with bistable SR, tristable SR has stronger advantages in weak signal extraction, but the classical tristable stochastic resonance (CTSR) system was limited by output saturation, which resulted in insufficient signal amplification ability. To solve the above problems, combined with asymmetric system whose output can be improved to a higher degree, a novel piecewise unsaturated asymmetric tristable SR (NPUATSR) system is proposed. Through numerical simulation, it is concluded that NPUATSR output amplitude varies proportionally with the amplitude of the input, which overcomes the output saturation of CTSR. Secondly, the stationary probability density and mean first passage time of particles are derived by using adiabatic approximation theory, and the variation law caused by parameters is analyzed in combination with potential function, the internal mechanism of the system is further studied. Through the output signal-to-noise ratio (SNR), it is found that the performance advantage of NPUTASR system is the most obvious, and different parameters affect the output SNR. Finally, the adaptive genetic algorithm is used to optimize the parameters, and the proposed system is applied to early fault diagnosis on different types of bearings. After comparison with different systems, the results show that NPUATSR can effectively detect the fault frequency, and has the most outstanding advantages in spectrum amplification and anti-noise performance, which proves that NPUATSR system has significant value in practical engineering application.

An Improved UKF Algorithm for Extracting Weak Signals Based on RBF Neural Network

It is difficult to extract effective weak signals from complex noise environment in many engineering applications. To address the problem, an improved unscented Kalman filter (UKF) algorithm based on a radial basis function (RBF)-neural network (NN) is proposed in this paper. This algorithm achieves effective extraction of weak signals under the condition of low signal-to-noise ratio (SNR). It uses RBF-NN to establish a distributed system of state and observation models to replace the traditional UKF modeling method. Then, the RBF-NN is applied to build a constant coefficient model to modify the UKF filtering deviation value, and thus, the accuracy of the algorithm is improved. Finally, simulation experiments are conducted at different SNRs of surface nuclear magnetic resonance signals. Results show that effective weak signals can still be obtained when SNR = -10 dB, verifying the effectiveness of the algorithm.

A Neural Network-Based Algorithm for Weak Signal Enhancement in Low Illumination Images

There is noise interference in low-illumination images, which makes it difficult to extract weak signals. For this reason, this paper proposes a low-illumination image weak signal enhancement algorithm based on neural network. Multi-scale normalization is performed on low-light images, and multi-scale Retinex is used to enhance weak signals in low-light images. On this basis, the GAN artificial neural network is used to detect the weak signal of the weak signal in the image, the normalization of the weak signal of the low-illumination image is completed based on the residual network, the self-encoding parameters of the depth residual are generated, and the weak signal enhancement result of the low-illumination image is output. The experimental results show that the method in this paper has better enhancement effect on low-illumination images and better image denoising effect. When the scale value is large, the low-contrast area of the low-illumination image has a better enhancement effect. The saturated area of the low-light image has a better enhancement effect.

Weak Signal Detection Based on Lifting Wavelet Threshold Denoising and Multi-Layer Autocorrelation Method

To solve the problem that the weak signal is difficult to detect under a strong background noise, a detection method based on lifting wavelet threshold denoising and multi-layer autocorrelation method is proposed. Firstly, the original signal is denoised by lifting wavelet threshold to improve the signal-to-noise ratio. Secondly, the multi-layer autocorrelation function of the noise-reconstructed signal is calculated, and its time-frequency signature are analyzed. Finally, the combined algorithm is used on weak signals with low signal-to-noise ratio to extract weak signal features. Simulation and experimental results demonstrate that the proposed method can detect weak signal features buried in the heavy noise effectively. The proposed method is compared with the traditional noise reduction method, which reflects its effectiveness and superiority.

A dual-wavelength bandpass Faraday anomalous dispersion optical filter operating on the D1 and D2 lines of rubidium

A dual-wavelength bandpass Faraday anomalous dispersion optical filter (DW-FADOF) operating at both the D1 and D2 lines of rubidium is experimentally demonstrated. The effects of certain system parameters, such as the incident laser power, magnetic field strength, and atomic density, on the performance of the filter are experimentally and theoretically investigated. Through elaborate simulation, an appropriate set of system parameters is selected, and the DW-FADOF achieves peak transmittance of 37.4% with a figure of merit (FOM) of 0.10 GHz−1 in the D1 line and 77.9% with a FOM of 0.33 GHz−1 in the D2 line, simultaneously. This FADOF provides transmission bands covering both two typical atomic transitions, and it can be used as a dual signal carrier. Due to the integration and minimization of loss, the results confirm the significant application potential of this filter in wavelength division multiplex communication and weak signal extraction.

A detection method of gear micro-cracks based on tuned resonance

Detection of micro-cracks before assembling has always been a challenge in gear defect detection. A mature detection method of micro-cracks is not available. Due to the complex detection environment and large production of gears, it’s urgent to develop a rapid and accurate detection method of gear micro-cracks. It’s the key to separate and extract weak vibration signals accurately under complex disturbances in gear defect detection. In this study, vibration characteristics of gear defects under resonance conditions were firstly explored and a detection method of gear micro-cracks was proposed based on tuned resonance. Based on the amplification effect of tuned resonance on the peak frequency spectrum of gear vibration, meshing frequency and sideband frequency characteristics of the gear defect signal spectrum were experimentally investigated. The amplitudes of gear meshing frequency and its harmonic meshing frequency in the signal frequency domain diagram were higher and the surrounding sideband frequency components were clearer.

Application of multi-layer denoising based on ensemble empirical mode decomposition in extraction of fault feature of rotating machinery

Aiming at the problem that the weak features of non-stationary vibration signals are difficult to extract under strong background noise, a multi-layer noise reduction method based on ensemble empirical mode decomposition (EEMD) is proposed. First, the original vibration signal is decomposed by EEMD, and the main intrinsic modal components (IMF) are selected using comprehensive evaluation indicators; the second layer of filtering uses wavelet threshold denoising (WTD) to process the main IMF components. Finally, the virtual noise channel is introduced, and FastICA is used to de-noise and unmix the IMF components processed by the WTD. Next, perform spectral analysis on the separated useful signals to highlight the fault frequency. The feasibility of the proposed method is verified by simulation, and it is applied to the extraction of weak signals of faulty bearings and worn polycrystalline diamond compact bits. The analysis of vibration signals shows that this method can efficiently extract weak fault characteristic information of rotating machinery.

The characteristic analysis of stochastic resonance in a time polo-delayed tristable system and its application

In the existing studies of stochastic resonance (SR) in time-delayed tristable systems, scholars have mainly studied the effect of time global delay on the system. Therefore, a time polo-delayed tristable system is studied in this paper. First, by using the small delay approximation method, the effective potential function Ueffx and steady-state probability density (SPD) are obtained. The influence of system parameters a, b,c, and time-delayed parameter τ on Ueffx is analyzed. The SPD plays an essential role in the research of particle motion state, and the effects of different system parameters on it are also analyzed by simulation. Then, by adiabatic approximation theory, the signal-to-noise ratio (SNR) is derived. The resonance characteristics of the time polo-delayed tristable stochastic resonance (TPTSR) system are studied by SNR. The simulation results show that the SNR is non-monotonic and SR can occur by adjusting the parameters. Finally, the TPTSR system is used to weak characteristic signal extraction and bearing fault diagnosis, and is compared with the high-order time-delayed feedback tristable stochastic resonance (HTFTSR) system. The results show that the weak signal can be detected and the failure frequency can be identified. In addition, the TPTSR system is better than the HTFTSR system in fault diagnosis, which shows that the system has important value in practical engineering applications.