Weak Detection Method Research Articles

Adaptive Measurement and Parameter Estimation for Low-SNR PRBC-PAM Signal Based on Adjusting Zero Value and Chaotic State Ratio

Accurately estimating the modulation parameters of pseudorandom binary code–pulse amplitude modulation (PRBC–PAM) signals damaged by strong noise poses a significant challenge in emitter identification and countermeasure. Traditionally, weak signal detection methods based on chaos theory can handle situations with low signal-to-noise ratio, but most of them are developed for simple sin/cos waveform and cannot face PRBC–PAM signals commonly used in ultra-low altitude performance equipment. To address the issue, this article proposes a novel adaptive detection and estimation method utilizing the in-depth analysis of the Duffing oscillator’s behaviour and output characteristics. Firstly, the short-time Fourier transform (STFT) is used for chaotic state identification and ternary processing. Then, two novel approaches are proposed, including the adjusting zero value (AZV) method and the chaotic state ratio (CSR) method. The proposed weak signal detection system exhibits unique capability to adaptively modify its internal periodic driving force frequency, thus altering the difference frequency to estimate the signal parameters effectively. Furthermore, the accuracy of the proposed method is substantiated in carrier frequency estimation under varying SNR conditions through extensive experiments, demonstrating that the method maintains high precision in carrier frequency estimation and a low bit error rate in both the pseudorandom sequence and carrier frequency, even at an SNR of −30 dB.

Multi-frequency weak signal detection based on Liu-like chaotic synchronization system and its hardware circuit implementation

Considering the shortcomings of traditional chaotic systems in weak signal detection methods, such as the high threshold sensitivity requirement and the narrow detection frequency domain. This study proposes a novel three-dimensional chaotic synchronization system, and the dynamical of the system are exhaustively characterized using equilibrium points, phase diagrams, Lyapunov exponential spectra, and bifurcation diagrams. This method involves weak signal detection by means of chaotic synchronization control. Synchronization of a chaotic system using a backstepping synchronization method is used to detect weak signals by analyzing the synchronization error after the introduction of weak signals in a strong noise background. The chaotic system is implemented by hardware circuits, and the simulation of chaotic synchronization control and detection of weak signals from the perspective of circuits is carried out by circuit simulation software. Additionally, the frequency range within which the system is capable of weak signal detection is tested through extensive simulation experiments. Finally, multi-frequency signals detection experiments are performed. The experimental results demonstrate that the system can accurately detect the frequency of weak signals address the limitations of narrow-band detection and multi-frequency signal detection is possible. Meanwhile, the circuit structure proposed in this paper is simple and has some value for engineering applications.

Weak signal detection based on pulse width counting method for underwater wireless optical communication with an analog mode PMT detector.

This work proposes a weak signal detection method based on pulse width counting (PWC) for the on-off keying (OOK) underwater wireless optical communication (UWOC) system with an analog mode photomultiplier tube (PMT) detector. The signal output model of the analog mode PMT in weak light communication and the influence of pulse overlap are investigated. We experimentally evaluate the proposed algorithm under different sampling rates, detection thresholds, data rates as well as received optical powers (ROPs), and compare the performance of the proposed approach with that of pulse amplitude detection and pulse peak counting. A 10-Mbps OOK UWOC link is realized with a sensitivity of -71.5 dBm in a 7-meter tank, which is 1.1-dB and 3.8-dB lower than that of pulse peak counting (PPC) and pulse amplitude detection (PAD) methods, respectively, and the total link attenuation is 94.8 dB. This system utilizes the analog mode PMT with larger dynamic range than photon-counting mode PMT to achieve weak light signal detection, which benefits design long-range UWOC systems.

A novel underwater weak signal detection method based on parameter optimized VMD and 3D chaotic system

Based on chaos theory, combined with improved variational mode decomposition, a novel method for underwater weak signal detection and frequency extraction is proposed. The aim of this research is to address the challenge of detecting underwater signals and extracting their frequencies under ultra-low Signal-to-Noise Ratio (SNR). A novel three-dimensional (3D) chaotic system with infinitely shrinking attractors is proposed, which incorporates a cosine function to achieve chaos control and enhancement. The nonlinear characteristics of the system, such as Lyapunov exponent and bifurcation, are analyzed in detail. Then, we design a frequency array and perform scale transformation on the chaotic system to construct a detection model that covering all frequencies. The model can transition from critical chaos to large period or intermittent chaos in the presence of external periodic signals by adjusting the parameters of the cosine function. Considering the relationship between the generation of intermittent chaos and the frequency of target signal, a frequency extraction algorithm based on chaos sparrow search algorithm optimization variational mode decomposition (CSSA-VMD) and Hilbert transform is designed. The algorithm can estimate the real frequency of underwater signals by performing noise reduction and envelope analysis on intermittent chaotic signals. The experimental results demonstrate that the proposed system can detect weak signals in complex marine backgrounds and extract their frequency features, with a detection SNR of -48 dB and a frequency error of less than 0.01%. Importantly, the circuit of the detection system has been implemented, verifying its feasibility for detecting and tracking underwater signals.

Weak signal detection method based on nonlinear differential equations

With the rapid development of computer network technology, it is often necessary to collect weak signals to collect favorable information. The development of signal detection technology is ongoing; however, various issues arise during the detection process. These issues include low efficiency and a high signal noise threshold. However, many problems will be encountered in the process of detection. In order to solve these problems, the nonlinear chaos theory is introduced to detect signals, and the simulation experiments of weak pulse signals and weak partial discharge signals are carried out respectively. The experimental results showed that the detection effect was remarkable in the quasi periodic state, and it had a good detection effect for weak pulse signals. At a signal-to-noise ratio of -25 dB, double coupling system, two-way ring coupling system, and single ring coupling system displayed detection success rates exceeding 98%. Meanwhile, the detection success rate of the strong coupling system was only 12%. Even at a noise signal ratio as low as -40 dB, the dual coupling system still maintained a detection success rate above 80%. The simulation results of partial discharge signal detection showed that there was a high fluctuation only at 2 ms, and the rest was basically stable at about 0 V, indicating that the system had a strong suppression effect on Gaussian white noise. When comparing the simulation results of the detection of the new chaotic system and the double coupling system, it was found that the new chaotic system has a superior impact in detecting weakly attenuated partial discharge signals. Through analysis of the system’s dynamic behavior, the research confirms its rich dynamic characteristics and sheds light on the reasons for phase state mutation and missed detection. The noise system is utilized for comparing the performance of various systems, with the goal of enhancing the system’s detection capability.

Weak pulse signal detection method based on improved strongly coupled oscillators

A strongly coupled oscillator can be used to detect weak pulse signals and recover waveforms, but its detection frequency of weak pulse signal is limited by the system’s built-in frequency. With a fixed built-in frequency, the system can only effectively detect and recover pulse signals in a certain frequency range, and waveform distortion occurs when pulse signals of higher frequencies are detected. In this work, the relationship between the built-in frequency of the coupled oscillator and the frequency detection range of weak pulse signal is analyzed, and two kinds of improved strongly coupled oscillator structures are proposed to extend the frequency detection range of weak pulse signals. By introducing the nonlinear restoring force coupling term, the nonlinear restoring force strongly coupled oscillator can effectively retain the high-frequency component of the signal, and can also better retain the signal characteristics when the pulse signal is input at a higher frequency. By introducing the Van der Pol-Duffing oscillator, the two-oscillator strong coupling system strengthens the stability of the internal structure of the system, and also achieves the effect of expanding the frequency detection range of the pulse signal. In addition, based on the variable iteration step size and frequency correlation of chaos detection, a method of detecting unknown frequency pulse signals is proposed. Instead of changing the built-in frequency of the system for frequency scanning, the method of changing the iteration step size is used. And using the frequency correlation of chaos detection, the correlation coefficient of the received signal and the recovered signal is compared with the correlation coefficient of the pure noise input case, then the pulse signals can be effectively detected based on the apparent difference between the two correlation coefficients. It is verified by simulation experiments that the proposed method can effectively detect the pulse signal of unknown frequency, and the proposed improved strong coupling oscillator has a greater performance improvement than that of the strong coupling oscillator.

Stochastic resonance induced weak signal enhancement in a second-order tri-stable system with single-parameter adjusting

Weak signal detection methods based on stochastic resonance (SR) have been extensively studied due to their capability to utilize noise energy for enhancing weak signals. Among various SR models, the second-order tri-stable SR models have demonstrated their superiority in weak-signal detection with better output performance compared to other SR models. To optimize the output performance of the second-order tri-stable systems, a variety of parameter optimization methods have been proposed to optimize the parameters of the system. However, these optimization methods often require to optimize multiple parameters, which leads to an increase in computational costs and reduces the real-time processing efficiency of signal processing. Such multi-parameter optimization methods cannot meet the demands for timely signal processing in the context of big data. To address this challenge, this paper proposes two single-parameter-adjusting SR models. The proposed models can attain an ideal output performance by adjusting a single parameter in the SR system. The spectral amplification as an indicator is derived to quantitatively analyze the effects of the proposed models on SR output. On this basis, the influences of the proposed models on the SR output under different potential well parameters, noise intensities, signal frequency, and damping ratio are fully investigated through numerical simulations. At last, the proposed models are employed to process an experimental signal with a weak fault feature, and the experimental results verify the feasibility of the proposed models in optimizing the SR output. The research results can guide the design of tri-stable SR models and support the application of the SR-based signal processing model in the context of big data.

Weak Fault Diagnosis Method of Rolling Bearings Based on Variational Mode Decomposition and a Double-Coupled Duffing Oscillator

Aiming at the problems of the low detection accuracy and difficult identification of the early weak fault signals of rolling bearings, this paper proposes a method for detecting the early weak fault signals of rolling bearings based on a double-coupled Duffing system and VMD. The influence rule of system initial value on the response characteristics of a double-coupled Duffing system is studied, and the basis for its determination is given. The frequency of the built-in power of the system is normalized, and a variance evaluation standard for the output value of the double-coupled Duffing system for weak fault signals detection is established. In order to solve the interference problem of fault monitoring signals, VMD is proposed to pre-process the fault monitoring signals. The weak fault signal detection method proposed in this paper is tested and verified by simulation signals and rolling bearing fault signals. The results show that the method proposed in this paper can detect the weak fault signal with the lowest signal-to-noise ratio reduced by 2.96 dB compared with the traditional Duffing detection system, and it can accurately detect the early weak fault signal of rolling bearings.

Preface

The 2023 6th International Conference on Mechanical, Electrical, and Material Applications (MEMA 2023) was successfully held in Changsha, China from February 24th to 26th, 2023 (virtual event). This Seminar was the sixth in a series of topical meetings, and the MEMA series of conferences have come to all successful conclusions in Xi’an, Chongqing, and Chengdu from 2018 to 2022. MEMA 2023 was attended by about 150 participants from different countries.For this fourth meeting we chose Changsha to be our Conference Venue and met with our old and new friends again. While the location of the Conference has been changed sometimes, what remained invariant is the aim of these meetings, which is to discuss recent advances and key challenges and explore new perspectives and research directions facing the development of mechanical, electrical, and material applications in a pleasant and friendly atmosphere. At this meeting we had a comprehensive overview of this fascinating field and of future scenarios thanks to the participation of leaders of the most important projects.The Conference presented an outstanding program of papers covering the most recent advances in mechanical, electrical, and material applications, including Mechanical Dynamics, Precision Machinery, Electromagnetic Compatibility, High Voltage and Insulation Technology, Circuits and Systems, Functional Materials, etc.. The papers in this Proceedings published by Journal of Physics: Conference Series-IOP Publishing represent a collection of the invited talks and academic contributions.The Conference Program consisted of four invited speeches presented in the plenary sessions, including four hot topic speeches highlighting the most recent advances in the field, and various oral presentations. The program included a speech by Dejie Yu who is Member of the Standing Committee of the Hunan Provincial CPPCC and Professor in the College of Mechanical & Vehicle Engineering of Hunan University, a speech title with “Research on Weak Fault Signal Detection Method Based on Gradient Acoustic Metamaterials”. Aiming at the problem that weak signal is difficult to detect, an enhanced detection method based on gradient index acoustic metamaterial was proposed. And still a fault signal enhancement detection method based on gradient index acoustic metamaterial was proposed.We would like to thank the participants, especially those who contributed speeches, posters and manuscripts, for making MEMA 2023 such an exciting and memorable conference. We thank the Technical Program Committee for putting together an outstanding Program. Finally, we thank the Local Organizing Committee for their tireless and expert efforts in the organization of MEMA 2023, and all of our colleagues whose friendly and efficient service contributed much to the success of the Conference.The Committee of MEMA 2023List of Committee Member is available in the pdf

Automated weak signal detection and prediction using keyword network clustering and graph convolutional network

Weak signals are rarely identified in the initial stage of growth and appear significant over time, unlike strong signals clearly observed in past trends. Weak signals are important cues that need to be analyzed to rapidly and accurately predict changes in the uncertain future. Researchers have developed various methods for identifying cues that can be significantly used for prediction. However, in many cases, they heavily depend on the opinions of experts or are applicable only to weak signals in specific fields. This study proposes a weak signal detection method that extracts weak signals by selecting significant keywords from literature database and grouping relevant keywords. Furthermore, this study presents a weak signal prediction method for predicting the growth of specific weak signals by investigating and learning the growth of the extracted weak signals over 10 years. To verify the proposed method, we extracted weak signals for 10 years (2001–2010) from SCOPUS publication data from 1996 to 2009 and applied machine learning using a graph convolutional network (GCN) model with the growth data of the extracted weak signals. The results showed that the proposed methods can effectively detect and predict weak signals.

Weak signal detection method based on novel composite multistable stochastic resonance

The weak signal detection method based on stochastic resonance is usually used to extract and identify the weak characteristic signal submerged in strong noise by using the noise energy transfer mechanism. We propose a novel composite multistable stochastic-resonance (NCMSR) model combining the Gaussian potential model and an improved bistable model. Compared with the traditional multistable stochastic resonance method, all the parameters in the novel model have no symmetry, the output signal-to-noise ratio can be optimized and the output amplitude can be improved by adjusting the system parameters. The model retains the advantages of continuity and constraint of the Gaussian potential model and the advantages of the improved bistable model without output saturation, the NCMSR model has a higher utilization of noise. Taking the output signal-to-noise ratio as the index, weak periodic signal is detected based on the NCMSR model in Gaussian noise and α noise environment respectively, and the detection effect is good. The application of NCMSR to the actual detection of bearing fault signals can realize the fault detection of bearing inner race and outer race. The outstanding advantages of this method in weak signal detection are verified, which provides a theoretical basis for industrial practical applications.

Research on weak signal detection method for power system fault based on improved wavelet threshold

Corresponding weak signals will be generated in case of power system failure in order to ensure the safe and stable operation of power system and detect the weak signal in real time, a denoising method based on improved wavelet threshold algorithm is proposed by analyzing the change of weak signal under noise interference. Firstly, the monitoring signal is decomposed by EMD. Secondly, each IMF component is transformed by wavelet transform to eliminate the noise in the wavelet coefficients of each scale. Finally, the denoised signal is obtained through the signal reconstruction mechanism. The experimental results show that the denoising method based on the improved wavelet threshold method not only removes the noise, but also retains more fault information features. Therefore, this method overcomes the interference of noise on signal stability and detects weak signals accurately and effectively. When there is strong noise in the power system, this method can still accurately observe the weak signal, effectively shield the influence of noise on the signal, improve the detection efficiency and accuracy, and is of great significance to ensure the stable operation of the power system.

Parameter-tuning stochastic resonance as a tool to enhance wavelength modulation spectroscopy using a dense overlapped spot pattern multi-pass cell.

The parameter-tuning stochastic resonance (SR) method can convert part of the noise energy into the signal energy to suppress the noise and amplify the signal, comparing with traditional weak periodic signal detection methods (e.g., time average method, filtering method, and correlation analysis method). In this work, the numerical calculation is conducted to find the optimal resonance parameters for applying the SR method to the wavelength modulation spectroscopy (WMS). Under the stochastic resonance state, the peak value of 2f signal (a constant concentration of CH4∼20 ppm) is effectively amplified to ∼0.0863 V, which is 3.8 times as much as the peak value of 4000-time average signal (∼0.0231 V). Although the standard deviation also increases from ∼0.0015 V(1σ) to ∼0.003 V(1σ), the SNR can be improved by 1.83 times (from ∼25.9 to ∼15.8) correspondingly. A linear spectral response of SR 2f signal peak value to raw 2f signal peak value is obtained. It suggests that the SR method is effective for enhancing photoelectric signal under strong noise background.

A Novel E-Exponential Stochastic Resonance Model and Weak Signal Detection Method for Steel Wire Rope

Steel wire rope can be frequently damaged by various factors. Although weak defect signal detection is the key to guarantee safety, current classic stochastic resonance (SR) models and methods are unavailable due to the complicated interference of noise. Thus, in this article, a novel E-exponential SR model and weak signal detection method are proposed. The principle of this model is first introduced from the perspective of Taylor expansion and nonlinear system analysis. Then, the model performance is simulated and evaluated by output signal analysis and chaos characterizations. Afterward, model comparison with different system parameters, experiments, and case studies regarding three typical wire rope defects inspection and weak signal recognition is conducted to demonstrate the validity of the novel method. Finally, the advantages and limitations of the proposed SR model are discussed.

A Quantum Weak Signal Detection Method for Strengthening Target Signal Features under Strong White Gaussian Noise

As the noise power increases, the target signal features become less obvious, which leads to the failure of weak signal detection methods. To address this problem, a quantum weak signal detection method, Local Semi-Classical Signal Analysis-Singular Value Decomposition (LSCSA-SVD), for strengthening target signal features under strong white Gaussian noise is proposed. Firstly, the time domain weak signal is quantized by the Schrodinger operator and its discrete spectrum formula. Then, in the quantum domain, the later eigenvalues are used to reconstruct the time domain signal, which can protect and enhance the target signal features. Finally, the difference between signal and noise in the singular value vector is used to further extract the reconstruction signal features. In simulation, the LSCSA-SVD can accurately extract target signals from white Gaussian noise signals with a signal-to-noise ratio (SNR) of −30 dB, which is better than the comparison methods. In the experiment, the weak acceleration sensor signal and the weak signal of the test circuit are successfully extracted. The results show that the LSCSA-SVD can suppress strong noise and improve the SNR.

Improved variable scale-convex-peak method for weak signal detection

Based on the rectification method of detectable domain and elimination method of blind domain, the variable scale-convex-peak method for the identification frequency of weak signal is improved. For the initial phase of weak signal to be detected, the analytic expression of chaotic threshold is obtained by using the stochastic Melnikov method. The influence of phase on chaotic threshold is clarified, which leads to that the frequency of weak signal can be identified. Specifically, within a period, the initial phase can be divided into the detectable domain and the blind domain. In the detectable domain, when the frequency identification deviates, the rectification method of detectable domain is introduced. Specifically, the Newton interpolation method is used to give the calculation formula to rectify the deviation, which improves the accuracy of frequency identification. In the blind domain, the elimination method of blind domain is given by constructing the detection equations. The feasibility of the improved variable scale-convex-peak method is verified by numerical simulation and circuit experiment. Furthermore, by comparing the identified frequency value with the theoretical fault frequency value, the fault location of the wheelset bearing of the high-speed train is determined. The improved variable scale-convex-peak method is applied when the initial phase is in the frequency detectable domain or the blind domain, respectively.

A novel method for UWB weak signal detection based on stochastic resonance and wavelet transform

Pulse ultra-wideband (UWB) signal is a transient pulse in time domain with very low power spectral density; In the long-distance communication, it is difficult to detect the low signal to noise ratio (SNR) pulse UWB signal submerged in strong noise. To solve the above problems, a novel adaptive stochastic resonance fusion wavelet transform method for UWB weak signal detection is proposed. Firstly, the adaptive optimization method which is based on the improved stochastic resonance potential well model is used to match the optimal parameters of the stochastic resonance (SR) system for the noisy signal, so that the noise energy can be transferred to the UWB signal and the signal amplification can be realized; Secondly, in view of the problem that SR method is insensitive to phase transient information and amplitude shift, wavelet transform is used to extract mutation information, so as to realize the accurate detection of UWB weak signal. The simulation results show that the proposed method can solve the problem that it is difficult to distinguish the change of signal state by using stochastic resonance method alone. Finally, two groups of actual signals with bandwidth of 10 MHz and 20 MHz are collected by the experimental system to test the effectiveness of the method.

Periodic‐phase‐diagram similarity method for weak signal detection

AbstractThe periodic‐phase‐diagram similarity method is proposed to identify the frequency of weak harmonic signals. The key technology is to find a set of optimal coefficients for Duffing system, which leads to the periodic motion under the influence of weak signal and strong noise. Introducing the phase diagram similarity, the influences of strong noise on the similarity of periodic phase diagram are discussed. The principle of highest similarity of periodic phase diagram with the same frequency is detected by discussing the persistence of similarity of periodic motion phase diagram under the strong noise and the periodic‐phase‐diagram similarity method is constructed. The weak signals of early fault and strong noise are input into Duffing system to obtain the identified system. The stochastic subharmonic Melnikov method is extended to obtain the conditions of the optimal coefficients for the identified system. Based on the results, the constructed frequency conversion harmonic weak signals are considered to form a datum periodic system. With the change of frequency in the datum periodic system, the phase diagram similarity of the two constructed systems can be calculated. Based on the periodic‐phase‐diagram similarity method, the frequency of weak harmonic signals can be identified by the principle of highest similarity of periodic phase diagram with the same frequency. The results of numerical simulation and the early fault diagnosis results of actual bearings verify the feasibility of the periodic‐phase‐diagram similarity method. The accuracy of the detection effect is 97%, and the minimum signal‐to‐noise ratio is −80.71 dB.

Multi-Frequency Weak Signal Decomposition and Reconstruction of Rolling Bearing Based on Adaptive Cascaded Stochastic Resonance

In engineering practice, the bearing fault signal is composed of a series of complex multi-component signals containing multiple fault characteristics information. In the early stage of fault sprouting and evolution, the fault features are easily disturbed by noise and irrelevant signals, eliminating the fault signals in the strong background noise. To overcome the influence of noise on the signal, this study proposes multi-frequency weak signal decomposition and reconstruction of rolling bearing based on adaptive cascaded stochastic resonance. First, the original signal is passed through the Hilbert transform to obtain the envelope signal. The envelope signal is high-pass filtered to eliminate the interference of low-frequency components on the response of the stochastic resonance system. Secondly, cascaded stochastic resonance system parameters are adaptively optimized by the quantum particle swarm algorithm (QPSO). The high-pass filtered signal input to the adaptive cascaded stochastic resonance system (ACSRS) can further enhance the weak fault characteristics, allowing the gradual transfer of high-frequency noise energy to the low-frequency fault characteristic components. Finally, the signal is decomposed using the variational mode decomposition (VMD) method to jointly determine the location of the fault characteristic frequencies in the intrinsic mode functions (IMF) component by the energy loss coefficient and correlation coefficient to achieve the reconstruction of multi-frequency weak signals. Through simulation and experimental validation, the effectiveness and superiority of the method for multi-frequency weak signal detection in bearings are verified. The results show that the method not only achieves the adaptive optimization of the stochastic resonance system parameters gradually removing the high-frequency noise in the signal and improving the energy of the low-frequency signal but also reduces the number of decomposition layers of the VMD, enhances the fault characteristic information in the weak signal, and effectively identifies the early weak fault characteristics of rolling bearings.

Chaos control and chaos synchronization of a multi-wing chaotic system and its application in multi-frequency weak signal detection

In this paper, first, a nonlinear feedback controller for achieving chaos control of a novel multi-wing chaotic system is presented. The nonlinear feedback controller has two parts. The first part is used to compensate an equilibrium point for the multi-wing chaotic system. The second part is a linear state feedback controller. The nonlinear feedback controller can globally asymptotically stabilize the multi-wing chaotic system to the equilibrium point. Stability conditions are given by using the Barbashin–Krasovskii theorem. Then, a linear state feedback controller for achieving chaos synchronization of the multi-wing chaotic system is presented. The linear state feedback controller can asymptotically stabilize the chaos synchronization error system to the origin. Stability conditions are given by using the passivity-based theory. Finally, a multi-frequency weak signal detection method is presented based on chaos control of the multi-wing chaotic system. The detection method can detect the frequencies of the weak signal and does not need to determine the critical point.