Weak Detection Method Research Articles

Research on weak signal detection of integral average digital lock-in amplifier

In the process of detecting the amplitude and phase of the weak signal based on digital lock-in amplifier (DLIA), a low-pass filter (LPF) with an extremely low cut-off frequency is required. Even so, the effect is often not good. The detection results of amplitude and phase of weak signal are distorted and fluctuate sharply. To eliminate the difficulty of LPF design, and to reduce the distortion and output fluctuation of detection results of DLIA, this paper proposes a weak signal detection method based on integral average digital lock-in amplifier (IADLIA), which can be used for weak signal detection under strong noise. This method optimizes the structure of the traditional DLIA based on the principle of cross-correlation detection. And then this paper analyzes the influence of the cut-off frequency and signal-to-noise ratio of the LPF on the detection accuracy and output stability of the weak signal amplitude and phase. Finally, the corresponding weak-signal amplitude at the bearing rotating frequency is accurately detected by the proposed method, and the accurate identification of bearing faults is realized. The experimental results show that the IADLIA is characterized by stable output, strong anti-interference ability and high output accuracy. The proposed method can accurately detect the amplitude and phase information of the weak signal in a strong noise environment, get rid of the structure of LPF, and the overall performance is better than the traditional DLIA.

Rotating machinery weak fault signal detection method based on QPSO and stochastic resonance

Due to the weak early vibration signal of rotating machinery, which is nonlinear and non-stationary, the traditional filtering method has some limitations. To this end, a weak signal detection method combining Quantum Particle Swarm Optimization (QPSO) and Stochastic Resonance (SR) is proposed. First, according to the adiabatic approximation theory, make the original signal meet the SR requirements, and optimize the structural parameters of the SR through QPSO; secondly, the signal-to-noise ratio is used as the objective function to make the input signal and the output signal achieve the best match; finally, simulation and experimental data are used to verify the feasibility of the proposed method. Comparative analysis shows that the proposed method increases the amplitude by 46% compared with the characteristic frequency obtained by the PSO optimized SR model.

A sea clutter detection method based on LSTM error frequency domain conversion

Weak signal detection has always been a hot spot in the signal processing field. In this paper, the chaotic and large data size characteristics of sea clutter are analyzed, the advantageof the Long and Short Term Memory network (LSTM) is taken to design a weak signal detection method based on deep learning. The reconstructed phase space signal is used as the input of LSTM network, the length of training data is determined by embedding dimension and delay time, and a chaotic prediction model is established to detect weak signals from the prediction error. In order to improve the detection performance, reduce the missing rate of deep learning method for small feature signal, frequency domain conversion of the prediction error is conducted, the spectrum of the prediction error of different distance gates is compared to locate the coordinates of the weak signal. The experimental results show that the sea clutter detection method based on LSTM prediction error frequency domain conversion has strong applicability and higher accuracy, and the detection performance is improved by 30%.

Theory and Application of Weak Signal Detection Based on Stochastic Resonance Mechanism

Stochastic resonance is a new type of weak signal detection method. Compared with traditional noise suppression technology, stochastic resonance uses noise to enhance weak signal information, and there is a mechanism for the transfer of noise energy to signal energy. The purpose of this paper is to study the theory and application of weak signal detection based on stochastic resonance mechanism. This paper studies the stochastic resonance characteristics of the bistable circuit and conducts an experimental simulation of its circuit in the Multisim simulation environment. It is verified that the bistable circuit can achieve the stochastic resonance function very well, and it provides strong support for the actual production of the bistable circuit. This paper studies the stochastic resonance phenomenon of FHN neuron model and bistable model, analyzes the response of periodic signals and nonperiodic signals, verifies the effect of noise on stochastic resonance, and lays the foundation for subsequent experiments. It proposes to feedback the link and introduces a two-layer FHN neural network model to improve the weak signal detection performance under a variable noise background. The paper also proposes a multifault detection method based on the total empirical mode decomposition of sensitive intrinsic mode components with variable scale adaptive stochastic resonance. Using the weighted kurtosis index as the measurement index of the system output can not only maintain the similarity between the system output signal and the original signal but also be sensitive to impact characteristics, overcoming the missed or false detection of the traditional kurtosis index. Experimental research shows that this method has better noise suppression ability and a clear reproduction effect on details. Especially for images contaminated by strong noise (D = 500), compared with traditional restoration methods, it has better performance in subjective visual effects and signal-to-noise ratio evaluation.

Improved Variable Scale-Convex-Peak Method for Weak Signal Detection

Improved Variable Scale-Convex-Peak Method for Weak Signal Detection

Chaotic resonance in a fractional-order oscillator system with application to mechanical fault diagnosis

This paper is to generalize the research on chaotic resonance (CR) towards fractional-order chaotic systems and then develop a new technique for detecting weak signals embedded in strong background noise. For illustration, a fractional-order Duffing oscillator system is evaluated by means of bifurcation analysis, revealing the phenomenon of chaotic resonance, with the optimal driving amplitude falling within a chaotic interval. It is found that the weak signal can be amplified by the intrinsic fluctuations in the chaotic system instead of stochastic noise. Based on this investigation, a novel weak signal detection method is developed and successfully applied to mechanical fault diagnosis without the need of signal preprocessing. Extensive numerical results show that the signal-to-noise ratio of the incipient fault signal of machinery can be greatly improved.

Study on the mean first-passage time and stochastic resonance of a multi-stable system with colored correlated noises

The mean first-passage time (MFPT) and the weak signal detection method of stochastic resonance (SR) on multi-stable nonlinear system under color correlated noise are studied. Using the uniform color noise approximation method, the Fokker-Planck equation of the system is obtained, and the steady-state probability density function of the multi-stable system driven by the multiplicative noise and additive noise is derived. On the basis of this, the formula of MFPT is derived, and the influence of parameters on the MFPT is analyzed. The problem of weak signal detection under color noise background is studied based on multi-stable SR. The results of simulation and experiment show that the method can effectively extract the frequency feature of weak signal in the background of color noise.

End-to-end LDA-based automatic weak signal detection in web news

An extremely competitive business environment requires every company to monitor its competitors and anticipate future opportunities and risks, creating a dire need for competitive intelligence. In response to this need, foresight study became a prominent field, especially the concept of weak signal detection. This research area has been widely studied for its utility, but it is limited by the need of human expert judgments on these signals. Moreover, the increase in the volume of information on the Internet through blogs and web news has made the detection process difficult, which has created a need for automation. Recent studies have attempted topic modeling techniques, specifically latent Dirichlet allocation (LDA), for automating the weak signal detection process; however, these approaches do not cover all parts of the process. In this study, we propose a fully automatic LDA-based weak signal detection method, consisting of two filtering functions: the weakness function aimed at filtering topics, which potentially contains weak signals, and the potential warning function, which helps to extract only early warning signs from the previously filtered topics. We took this approach with a famous daily web news dataset, and we could detect the risk of the COVID19 pandemic at an early stage.

Extracting weak multi-frequency signal in heavy colored noise

The extraction of weak multi-frequency signal in the background of heavy colored noise is studied. For the multi-frequency signal which cannot be directly decomposed by ensemble empirical mode decomposition (EEMD), further processing is made in the paper. Based on the normalized autocorrelation analysis and EEMD, a new method of weak multi-frequency signal detection is proposed in the article. Firstly, the adaptive noise reduction of multi-frequency signal with strong colored noise is realized by the normalized autocorrelation analysis. Then, the denoised multi-frequency signal is decomposed by the EEMD method. A series of monochromatic intrinsic mode functions are obtained to representing the characteristics of multi-frequency signal. Finally, two mechanical fault experiments are designed to verify the applicability of the proposed method in the fault diagnosis. The vibration signals with bearing pedestal bolt looseness fault, bearing outer raceway and rolling element compound fault were collected, respectively. The experimental results show that the weak multi-frequency fault features in the vibration signals with heavy colored noise are effectively extracted by the new method. The proposed method has a good application prospect in the field of signal processing.

Machine Fault Detection under the Background of Strong Interference Based on Line Learning Music Algorithm

First, this article introduces the importance of fault diagnosis in social production, and analyzes many existing machine fault detection methods. Considering the use conditions of the algorithm, these methods may be complicated and may not be suitable for online applications. Especially for the background of strong interference, this phenomenon occurs in the early stage of machine failure, and the traditional weak signal detection method loses its function. The characteristics of random noise modulation are given, and then an improved online learning evolutionary MUSIC algorithm is designed. First, it constructs a data array based on the sampling sequence, and then analyzes the signal spectrum under the condition that the number of signals is underestimated. Because traditional analysis methods are limited to current data, it is difficult to obtain stable analysis results under strong interference. Finally, some historical analysis data is weighted to obtain the true spectrum. The effectiveness of the algorithm can be seen through simulation. For weak fault signals under strong interference background, this method has better detection performance than FFT algorithm.

Application of principal component analysis method for micro-resonator weak resonant signal detection.

The characteristic of weak resonant signal output by an electrothermally excited microresonator is analyzed, and the principal component analysis (PCA) method is proposed and applied in the resonant frequency detection of the output weak signal by separating the noises. Simulation on weak resonant signals under different levels of noises and different quality factors of the resonator was conducted after the influence of the data selection window width and the principal component number on detection results was analyzed. Finally, the experiment platform was built, and the output signal of the sensor sample was used to verify the detection effect of the proposed PCA method. Simulation and experiment results show that the proposed PCA method could accurately obtain the resonant frequency compared with the resonant frequency obtained by the smoothing filter and Lorentzian fitting method.

The TVICMs method for weak signal detection based on a nonlinear stochastic delay differential system

A nonlinear stochastic delay differential system with symmetry SD oscillator is investigated to detect weak signal. Firstly, the stochastic Melnikov function and the chaos threshold are obtained by using the stochastic mean square criterion. Secondly, the stochastic center manifold and the stochastic averaging method are used to obtain the one-dimensional Itoˆ equation and the corresponding FPK equation. The stationary probability density of the FPK equation is studied and the corresponding nonlinear stochastic dynamical behaviors are analyzed. Finally, the highlight of this paper is to detect the weak signal with ‘transient vacancy in chaotic motions’ (TVICMs) method successfully and extract the features information such as frequency and amplitude on the premise of unknown features information. Astonishingly, numerical simulation shows that the time delay can enhance the detection capability of the system and the effectiveness of the new method.

Human-autonomous devices for weak signal detection method based on multimedia chaos theory

The detection of weak signals has been widely used in communication, radar and other fields. The detection of weak signals in the background of strong noise is an important research hotspot of modern information theory, and prompts people to explore and study new theories and new methods of weak signal detection. The device takes Holmes-type Duffing mapping as the research object, and uses the Lyapunov exponent as the criterion for chaos identification. The chaotic critical value of the equation is changed from chaotic state to periodic state. Whether it contains the detection algorithm of the target signal, the signal detection for the unknown frequency and the Holmes-type Duffing system is improved by the sliding mode variable structure control method in the control theory. The simulation results show that the improved chaotic Duffing system can effectively suppress the noise and detect the frequency of the weak signal through the power spectrum of the system.

Weak Signal Detection Method Based on the Coupled Lorenz System and Its Application in Rolling Bearing Fault Diagnosis

Rolling bearings are widely used in rotating machinery. Their fault feature signals are often submerged in strong noise and are difficult to identify. This paper presents a new method of bearing fault diagnosis that combines the coupled Lorenz system and power spectrum technology. The process is achieved in the following three steps. First, a synchronization system based on the Lorenz system is constructed using the driving-response method. Second, when the tested signal is connected to the driving end, the synchronization error between the two sub-chaotic systems is obtained. Finally, the power spectrum density of the synchronization error is calculated and compared with the corresponding fault characteristic frequency. The coupled Lorenz system makes full use of the noise immunity and nonlinear amplification of the chaotic system. The detection characteristics and feasibility of the new method are verified by simulation and actual measured vibration data. The result shows that the noise reduction effect of the coupled Lorenz system is obvious. This method can improve the signal-to-noise ratio of the tested signal and provide a new way to perform fault diagnosis of rolling bearings.

Variable scale-convex-peak method for weak signal detection

A variable scale-convex-peak method is constructed to identify the frequency of weak harmonic signal. The key of this method is to find a set of optimal identification coefficients to make the transition of dynamic behavior topologically persistent. By the stochastic Melnikov method, the lower bound of the chaotic threshold continuous function is obtained in the mean-square sense. The intermediate value theorem is applied to detect the optimal identification coefficients. For the designated identification system, there is a valuable co-frequency-convex-peak in bifurcation diagram, which indicates the state transition of chaos-period-chaos. With the change of the weak signal amplitude and external noise intensity in a certain range, the convex peak phenomenon is still maintained, which leads to the identification of frequency. Furthermore, the proposition of the existence of reversible scaling transformation is introduced to detect the frequency of the harmonic signal in engineering. The feasibility of constructing the hardware and software platforms of the variable scale-convex-peak method is verified by the experimental results of circuit design and the results of early fault diagnosis of actual bearings, respectively.

Four‐dimensional dynamic force measurement system for micro‐deep hole drilling

An embedded micro-deep hole drilling force measurement system is proposed and verified experimentally, it is based on resistance strain gauge, weak signal detection method and embedded technology. The system can measure the thrust force, tangential force and two orthogonal components of radial force precisely, continuously and stably in real time, while other similar systems can measure only thrust force usually. The system can replace the complex modelling of drilling process to obtain random changing details of drilling forces, and can also be used effectively in intelligent drilling control research.

Research on detection and location of weak edge signals

In order to be able to control the growth rate of industrial polycrystalline silicon rods well and reduce energy consumption, it is necessary to accurately locate the boundaries of the silicon rods and measure the diameter of the silicon rod during growth. In this paper, a weak edge signal detection method based on small area is proposed for the problem of weak boundary signal in the late growth stage of polycrystalline silicon rods. The method increases the gradient of the edge by projecting the boundary signal in the column direction. In addition, the method further enhances the gradient information of the weak boundary signal by improving the classical difference operator. In the experimental part, the improved difference operator increases the weak difference signal value from 304 to 686. Finally, the effectiveness of the algorithm is proved by two groups of experimental results.

An Improved Underdamped Asymmetric Bistable Stochastic Resonance Method and its Application for Spindle Bearing Fault Diagnosis

High-precision spindle bearing is one of the most critical and vulnerable parts in a motorized spindle. Its unexpected failure may lead to production loss. Stochastic resonance (SR) is a weak signal detection method, which can obtain noise energy in strong background noise and enhance incipient fault characteristics of spindle bearing. Based on the fact that asymmetry can improve the enhancement ability of asymmetric bistable SR in weak feature extraction, we introduce an underdamped well-width asymmetric bistable SR (UABSR) method to the field of bearing fault diagnosis for the first time. However, the engineering application of UABSR can still be limited by two aspects. Firstly, the SNR index can take effect only when the actual fault frequency is obtained in advance, so the UABSR method is at high-cost in real practices. Secondly, an appropriate band-pass filter band range of the bearing faults can hardly be obtained due to the massive impulsive noise in operations. Here an improved UABSR method for spindle bearing fault diagnosis is proposed. Infogram method is used to process and analysis the original vibration signal for resisting the influence from the impulsive noise and obtaining more accurate frequency range of spindle bearing fault. In addition, time domain zero-crossing (TDZC) index, as the index of the improved UABSR method, can directly reflect the fault characteristics of spindle bearings without knowing the accurate fault characteristic frequency in advance. Besides, the Quantum Genetic Algorithms (QGAs) and the fourth-order Runge-Kutta algorithm are combined to simultaneously obtain the optimal system parameter, the asymmetric ratio, the damping factor and the rescaling factor of the improved UABSR model. Comparing the Infogram and original UABSR methods, the improved UABSR method performs better effect in incipient spindle bearing fault diagnosis.

Theoretical research of point-measurement laser absorption spectroscopy

The point measurement laser absorption spectroscopy (PMLAS) based on saturated absorption theory could surpass the defect of 'line-of-sight' measurement in traditional tunable diode laser absorption spectroscopy (TDLAS) and achieve the 'point' measurement with millimeter spatial resolution. It is realized by crossing with two frequency synchronized laser beams: one named probe beam as in traditional TDLAS and the other named saturated beam with higher power. In this paper, the theory of PMLAS was firstly analyzed by the theoretical deduction of saturated absorption coefficients with arbitrary cross angles and the numerical calculations of point absorbance under different saturation parameters. Next, a weak signal detection method based on high-frequency sinusoidal modulation of the saturated beam intensity was proposed, in which the first-order harmonic signal was theoretically deduced and verified by numerical demonstration. Furthermore, it is found that the FWHMs (full width at half maximum) of different order harmonics are all the same and equal to the width of the absorption signal without modulation, which implied that the superposition of multi-harmonics could enhance the signal-to-noise ratio (SNR) in measuring the spectrum line-width.

Development and application of new technologies and equipment for in-line pipeline inspection

In-line inspection is one of the most effective technical measures to ensure the safety of oil and gas pipelines. However, the extensive application of oil and gas pipelines of high-carbon steel, large diameter, high pressure and high flow rate in recent years brings about new challenges to in-line inspection. In this paper, we investigated the engineering application of new technologies and equipment, including the in-line inspection technology based on electromagnetic detector array, the extremely low frequency transient weak signal detection technology and new in-line inspection equipment. Then, the new technology and equipment of electromagnetic detector array were developed by applying the active emission and receiving of electromagnetic signal to inspect the metallic defects. Finally, noisy signals were inspected on the basis of Duffing chaotic oscillator, and thus the inspection of extremely low frequency signals in the noise was realized. In addition, the new method and equipment of extremely low frequency transient weak signal detection were developed and verified in actual inspection engineering. And the following research results were obtained. First, the in-line inspection technology based on electromagnetic detector array uses the synergetic effect between DC exciting magnetic field and high-frequency exciting magnetic field, so a perturbation response can be realized even by small signal excitation. Second, with the introduction of a novel compressive sampling of acquisition information composition, the speed bottleneck of in-line detector is broken through, and a new world record of the inspection speed of the electromagnetic detector array is set up, i.e., 8 m/s. Third, under the condition of high-speed movement, the received signals of in-line detector are weak and temporary while tracking and positioning outside the pipeline. And by virtue of the extremely low frequency transient weak signal detection method based on chaos, this technical bottleneck is broken through, and the signal-to-noise ratio (SNR) of transient weak signal in the process of real-time inspection is decreased below −10 dB. In conclusion, the superior performance of these new equipment has already been verified in the inspection engineering of in-service oil and gas pipelines. These research results will provide technical and equipment support for the safe operation of domestic main oil and gas pipelines.