Simulated Signals Research Articles

Quantitative Analysis of the Hsu-Nielsen Source through Advanced Measurement and Simulation Techniques

Abstract This paper presents the results from conducting a series of experiments with a Hsu-Nielsen Source, accompanied by corresponding numerical simulations on a solid block. The aim being to illustrate a Finite Element Analysis (FEA) approach for simulating Acoustic Emission (AE) wave propagation in a Hsu-Nielsen Source, by employing virtual sensors to enhance existing AE research methodologies. The objective was to examine and establish the actual unload rate derived from Pencil Lead Breaks (PLBs) by comparing results from simulations and experimental trials. These experiments and simulations were conducted using a solid cylindrical steel block, capturing the propagating Acoustic AE waves from both sources over a two-second span. When comparing the experimental data with the simulation results, it is evident that replicating the structure of an impulsive AE source is feasible for brief durations. Furthermore, both the experimental and simulated signals on the steel cylinder displayed comparable patterns in the initial 25-30 µs. The methodology presented in this study demonstrates the effectiveness of Finite Element Analysis (FEA) in precisely identifying the specific modes present in AE wave propagation, including the actual unload rates affecting the AE signals recorded.

Digital twin-driven attention-guided convolutional networks for intelligent fault diagnosis across different domains

Fault diagnosis of rolling bearings is crucial in industries to ensure the reliability, safety, and economic feasibility of mechanical systems. Conventional data-driven methods for fault diagnosis depend on pre-existing datasets containing various failure modes for training. However, obtaining such datasets can be challenging, especially in critical industrial environments, hindering the applicability of these methods across different scenarios. To address this challenge, this research introduces a novel approach called a digital twin-driven attention-guided convolutional network (DTACN) for bearing fault diagnostics with limited data. This study offers two significant enhancements: (1) Development of an intricate digital twin model for bearings, which includes multi-scale Kinetic simulations of the bearing’s operational parameters. This model utilizes the architectural characteristics of the bearing and the severity of the failure to predict the vibratory system’s response accurately; and (2) Establishment of a domain adaptation-based framework employing deep convolutional models and attention mechanism to transfer knowledge from simulated signals to real signals. This framework facilitates efficient fault recognition even in scenarios with limited prior knowledge. The effectiveness of the proposed approach has been rigorously validated through comprehensive experiments.

Efficient defect reconstruction from temporal non-uniform pulsed thermography data using the virtual wave concept

In this study, we present an extension of the virtual wave concept to enable photothermal reconstruction from temporal non-uniform pulsed thermography data. Therefore, we introduce a generalized discrete transformation kernel, which allows to account for arbitrary temporal sampling strategies. First, we show the evidence of the proposed strategy for analytical temperature signals. Moreover, we demonstrate the advantages of the strategy for simulated temperature signals, obtained from an orthotropic sample with defect interfaces at various depth positions. For experimental verification, we apply pulsed thermography in the pulse-echo configuration for a carbon fiber-reinforced polymer sample with different embedded defects. It can be shown that efficient time sampling in the virtual wave concept allows a significant reduction in the number of data points compared to uniform sampling, without compromising the quality of the reconstruction results.

Delamination detection in composite pipes using higher harmonic generation of flexural waves

This paper investigates delamination detection in composite pipes using higher harmonic generation technique for the first time. The semi-analytical finite element (SAFE) method is developed to analyze the dispersion characteristics of the composite pipe. Then, a flexural mode at low frequency is selected to detect delamination. The three-dimensional (3D) finite element (FE) model is developed to simulate the flexural mode propagating in the composite pipe and its interaction with delamination. By comparing the simulated radial displacement signals with and without delamination, it is observed that the delamination can contribute to the higher harmonic generation. Subsequently, the ultrasonic measurements are conducted to demonstrate the numerical results. Using a relatively simple piezoelectric transducer (PZT) configuration, an almost pure flexural mode can be excited in the experiment. More importantly, experimental results indicate the higher harmonics induced by the delamination can be observed obviously. Finally, extensive parametric studies are performed to reveal the sensitivity of higher harmonics to the delamination with various sizes and at different interface. The proposed higher harmonic generation of flexural waves is promising for the practical application of delamination detection in composite pipes.

Cyclostationarity blind deconvolution via eigenvector screening and its applications to the condition monitoring of rotating machinery

Maximum second-order cyclostationarity blind deconvolution (CYCBD) is accomplished by maximizing the second-order cyclostationarity of signals through the indicator of second-order cyclostationarity (ICS2). It is a significant method for extracting weak periodic pulses related to bearing faults. However, since the interference spectral lines generated by the interference signals in squared envelope spectrum do not be considered by ICS2, the method can be invalid for certain applications. In addition, when the detected signal contains compound faults, only one fault can be enhanced, and misdiagnosis is easily caused. This article proposes cyclostationarity blind deconvolution via eigenvector screening, abbreviated as ESCYCBD, for fault feature enhancement and extraction of rotating machinery in order to solve these problems. Different from existing deconvolution methods which use the maximum value of the index as a criterion to select the filter coefficients, this method proposes the concept of eigenvector subspace, which contains the optimal eigenvector as filter coefficients that are ignored by CYCBD. Subsequently, the Fourier coefficients related to a series of cyclic frequencies are extended for compound fault signals. Besides, by using the harmonic characteristics of fault features in the envelope spectrum, fault feature recognition and optimal selection are carried out in eigenvector subspaces. At the same time, the concept of signals set is created, by which means different feature modes of different fault can be extracted at one time. The analysis results of simulation signals and experimental data show that the proposed ESCYCBD has robustness and accuracy in extracting fault feature modes, whether it is single fault diagnosis or compound fault diagnosis.

Simulation of Random Signals in Ghost Polarization

Simulation of Random Signals in Ghost Polarization

Adaptive time-domain impact extraction method for multi-source impact vibration signal of diesel engine

Diesel engines are widely used in fields such as ships, vehicles, and nuclear power. The vibration signals of a diesel engine’s casing exhibit a characteristic of intermittent distribution of multi-source impacts. In response to the challenges faced by existing feature extraction methods in identifying and localizing impact signals, this paper proposes the adaptive time-domain impact extraction (ATDIE) method, which is based on the characteristics of impact signals exhibiting local high-energy distribution in the time domain and rapid amplitude decay. The purpose of the ATDIE method is to extract various impact components from multi-source impact signals. The ATDIE method constructs a solution model with the goal of minimizing the signal’s multi-order amplitude central moments. By using the residual iterative solution method, the number of extracted components is adaptively determined. Then, an impact window optimization function is established to enhance the adaptability. Finally, the test results with both simulation signals and diesel engine signals demonstrate that the ATDIE method possesses good capabilities for impact extraction and computational efficiency.

A novel simulation-assisted transfer method for bearing unknown fault diagnosis

Abstract Supervised data-driven bearing fault diagnosis methods rely on completed datasets of faults, which can be challenging for signals collected in real engineering. Recognizing unknown faults using a data-driven approach is particularly difficult, as purposefully modeling these faults is complex. To address this challenge, this study proposes a new simulation-assisted transfer bearing unknown fault diagnosis method for realizing unknown compound fault diagnosis of rotating machinery. Firstly, finite element method is used to obtain the compound fault data that does not exist in the historical data, and wavelet packet transform is performed on the simulated and measured signals to enhance the detailed features of the signals. Then, a deep convolutional feature fusion network based on hybrid multi-wavelet spatial attention is constructed to fuse the time-frequency information processed by different wavelet bases. Finally, by integrating the concepts of intra-class splitting and transfer learning, the model is fine-tuned using simulation data to recognize unknown compound faults of rolling bearings. The method validates the simulated signals’ feasibility and the unknown faults’ diagnostic validity under the publicly available rolling bearings dataset. Compared to the comparison methods, the method’s accuracy increased by 2.86%, 2.61%, 5.41%, 4.77%, and 7.07%, respectively.

Fault diagnosis of high-speed motorized spindles based on lumped parameter model and enhanced transfer learning

Condition monitoring of rotating components is crucial for ensuring the reliability and safety of mechanical systems, and artificial intelligence (AI) plays a significant role in achieving the goal. However, the high costs and complexity associated with components like high-speed motorized spindles present significant challenges in collecting complete fault samples. Therefore, we propose a new approach to tackle the challenges. The process commences with establishing a lumped parameter dynamic model for the high-speed motorized spindle. Then, the parameters such as stiffness, eccentricity, and damping in the lumped parameter model were optimized using genetic algorithm. Subsequently, simulated fault samples are acquired by introducing excitation to normal simulated signals. Finally, transfer learning techniques are utilized for intelligent fault diagnosis. The training set consists of simulated fault samples, while the testing set comprises experimental fault samples. Our approach aims to enhance the efficiency and accuracy of fault diagnosis for high-speed motorized spindles while also addressing the challenge of high cost.

Fourth-stable stochastic resonance and its application to weak fault signal detection of rotating machinery

Weak characteristic extraction is vital for weak fault signal detection of machinery. Stochastic resonance (SR) is able to transfer noise energy into weak fault characteristic frequency excited by a defect of machines. However, the potential function in SR is vital to enhance weak fault characteristic frequency and determines the capability of SR to improve the signal-to-noise ratio (SNR) of a noisy signal. Now, common potential functions include monostable, bistable and even tri-stable potentials but fourth-stable SR has not been studied and applied to detect early fault characteristic frequency. In this paper, thus, we would investigate the behaviors of SR with a fourth-stable potential subject to additive noise, in which the approximate theoretical expression of the power done by SR is derived to demonstrate the fourth-stable Sr Then, a SR method with the fourth-stable potential is proposed to enhance weak fault characteristic frequency, in which these system parameters are adjusted by using SNR as the objective function and using genetic algorithms adaptively. In this paper, thus, Finally, the proposed method is verified by using a simulated signal with noise and two early fault experiment of rolling element bearings with different levels of defects on the outer and inner races. Moreover, the proposed method is compared with wavelet denoising and fast kurtogram methods. The comparisons indicate that the proposed method has the better performance for enhancing weak fault characteristic frequency or weak useful signals than other two methods and is available to weak fault signal detection of machinery.

Revisiting the dead time effects of Insight-HXMT/ME on timing analysis

ABSTRACT Dead time is a common instrumental effect of X-ray detectors, which would alter the behaviour of timing properties of astronomical signals, such as distorting the shape of power density spectra (PDS), affecting the root-mean-square of potential quasi-periodic oscillation signals, etc. We revisit the effects of the dead time of Medium Energy X-ray telescope (ME) onboard Insight-HXMT based on the simulation of electronic read-out mechanism that causes the dead time and the real data. We investigate dead time effects on the pulse profile as well as the quasi–periodic oscillation (QPO) signals. The dead time coefficient suggests a linear correlation with the observed count rate in each phase bin of the pulse profile according to the simulation of periodic signal as well as the real data observed on Swift J0243.6+6124. The Fourier-amplitude-difference (FAD) method could well recover the intrinsic shape of the observed PDS in the case that the PDS is from two identical detectors. We apply this technique on ME, by splitting the nine FPGA modules into two groups. The results indicate that the FAD technique suits the case when two groups of detectors are not largely different; and the recovered PDS of Sco X-1 observed by ME slightly enhances the significance of the previously known QPO signal, meanwhile the root-mean-square of QPO is significantly improved. We provide the FAD correction tool implemented in HXMTDAS for users in the future to better analyse QPO signals.

Research on a Fault Feature Extraction Method for an Electric Multiple Unit Axle-Box Bearing Based on a Resonance-Based Sparse Signal Decomposition and Variational Mode Decomposition Method Based on the Sparrow Search Algorithm.

In light of the issue that the vibration signal from an axle-box bearing collected during the operation of an electric multiple unit (EMU) is seriously polluted by background noise, which leads to difficulty in identifying fault characteristic frequency, this paper proposes a resonance-based sparse signal decomposition (RSSD) and variational mode decomposition (VMD) method based on sparrow search algorithm (SSA) optimization to extract the fault characteristic frequency of the bearing. Firstly, the RSSD method is utilized to decompose the signal based on the obtained optimal combination of quality factors, resulting in the optimal low-resonance component with periodic fault information. Then, the VMD method is performed on this low-resonance component. The parameter combinations for both methods are optimized utilizing the SSA method. Subsequently, envelope demodulation is applied to the intrinsic mode function (IMF) with maximum kurtosis, and fault diagnosis is achieved by comparing it with the theoretical fault characteristic frequency. Finally, experimental validation and comparison are conducted by utilizing simulated signals and example signals. The results demonstrate that the proposed method extracts more obvious periodic fault impact components. It effectively filters out the interference of complex noise and reduces the blindness of setting weights on parameters due to human experience, indicating excellent adaptability and robustness.

Research on Leak Detection of Low-Pressure Gas Pipelines in Buildings Based on Improved Variational Mode Decomposition and Robust Kalman Filtering.

Aiming at the complex characteristics of negative pressure waves in low-pressure pipelines inside of buildings, we proposed an estimation method of pressure fluctuation trends based on the robust Kalman filter and the improved VMD, which can be used for leakage detection. The reconstructed baseline signal can accurately describe the fluctuation trend of the negative pressure wave after the pressure drop, and quantitatively express the characteristic difference between the leakage condition and the gas usage condition. The robust Kalman filter was used to estimate the pressure fluctuations. The parameters of VMD were adaptively calculated based on the WAA and discrete scale space. The trend components contained in the IMFs were separated by a reconstruction based on the Fourier series. Based on the simulation signal, the method can accurately restore the trend component contained in the complex pressure signal. Based on the actual signals, the accuracy of small leakage detection is 96.7% and the accuracy of large leakage detection is 73.3%.

Direction of Arrival Joint Prediction of Underwater Acoustic Communication Signals Using Faster R-CNN and Frequency–Azimuth Spectrum

Utilizing hydrophone arrays for detecting underwater acoustic communication (UWAC) signals leverages spatial information to enhance detection efficiency and expand the perceptual range. This study redefines the task of UWAC signal detection as an object detection problem within the frequency–azimuth (FRAZ) spectrum. Employing Faster R-CNN as a signal detector, the proposed method facilitates the joint prediction of UWAC signals, including estimates of the number of sources, modulation type, frequency band, and direction of arrival (DOA). The proposed method extracts precise frequency and DOA features of the signals without requiring prior knowledge of the number of signals or frequency bands. Instead, it extracts these features jointly during training and applies them to perform joint predictions during testing. Numerical studies demonstrate that the proposed method consistently outperforms existing techniques across all signal-to-noise ratios (SNRs), particularly excelling in low SNRs. It achieves a detection F1 score of 0.96 at an SNR of −15 dB. We further verified its performance under varying modulation types, numbers of sources, grating lobe interference, strong signal interference, and array structure parameters. Furthermore, the practicality and robustness of our approach were evaluated in lake-based UWAC experiments, and the model trained solely on simulated signals performed competitively in the trials.

Numerical Simulation and Application of Vortex Field Monitoring near Islands in Straits

This study addresses the issue of vortex current fields near the islands and reefs in China’s straits, which pose significant challenges to engineering construction and navigation safety in the surrounding waters. To monitor these vortex fields, the study proposes an innovative method utilizing acoustic signals. The study utilizes the numerical simulation and phase feature extraction of acoustic signals in the vortex current field, based on ray acoustic theory and time-reversal mirror technology. The study successfully monitored the central position of the vortex core and characteristic radius of the vortex current field near Barley Straw Reef using HYCOM data for the first time. Furthermore, the performance of the method was analyzed under different acoustic phase perturbations and signal-to-noise ratios. The numerical simulation results demonstrate that the acoustic method is effective in monitoring near-shore vortex fields, and the time-reversal mirror technique is useful in extracting phase difference information from acoustic signals generated by vortex currents.

Simulation of radio signals from cosmic-ray cascades in air and ice as observed by in-ice Askaryan radio detectors

Simulation of radio signals from cosmic-ray cascades in air and ice as observed by in-ice Askaryan radio detectors

Probing flavor constrained SMEFT operators through tc production at the muon collider

We investigate flavor violating four-Fermi Standard Model Effective Field Theory (SMEFT) operators of dimension-six that can be probed via tct¯c+tc¯\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\left(\\overline{t}c+t\\overline{c}\\right) $$\\end{document} production at the multi-TeV muon collider. We study different FCNC and FCCC processes related to B, Bs, K and D decays and mixings, sensitive to these operators and constrain the corresponding couplings. The tensor operator turns out to be most tightly bound. We perform event simulation of the final state signal from tc production together with the SM background to show that operators after flavor constraint can reach the discovery limit at 10 TeV muon collider. We further adopt the optimal observable technique (OOT) to determine the optimal statistical sensitivity of the Wilson coefficients and compare them with the flavor constraints. We use the limits to predict the observational sensitivities of the rare processes like KL → π0ℓℓ, D0 → μμ, t → cℓℓ.

Distributed estimation cascade second-order tri-stable stochastic resonance method for random noise suppression in continuous-wave mud pulse telemetry

The extraction of high-quality useful signals in downhole environments has perennially posed a technical challenge for continuous wave mud pulse transmission systems. Traditional denoising methods, such as adaptive filtering and wavelet transform, inevitably compromise the integrity of the original signal's useful components while removing noise. Different from the traditional denoising methods, stochastic resonance (SR) is capable of transferring the energy of noise to the signal, thereby accomplishing the objective of noise suppression. In this paper, based on the second-order tri-stable stochastic resonance detection method combined with the estimation of distribution algorithm (EDA), a distributed estimation cascade second-order tri-stable stochastic resonance (EDA- CSTSR) detection method is proposed for the first time. By constructing 5 groups of simulation signals with different signal-to-noise ratios (SNRs), the denoising performance of distributed estimation CSTSR, Langevin SR, and Duffing SR is analyzed in detail. The results show that compared with Langevin SR and Duffing SR, the EDA-CSTSR can increase the SNR of the input simulation signal by at least 17 dB and significantly increase the amplitude of the useful pulse signal. Moreover, the EDA-CSTSR is applied to process actual data obtained from a drilling site, enabling the recognition of weak signals amidst strong background noise, which highlights the method's excellent practical application value and underscores its potential for further enhancement.

Optimized dispersion Higuchi fractal dimension and its refined composite multi-scale version for signal analysis

Higuchi fractal dimension (HFD), as a classic nonlinear dynamic metric, which is commonly used to detect signal dynamic changes. However, it is difficult for HFD to process signal outliers. To address this issue, dispersion HFD (DHFD) is proposed, which improves the signal complexity representation ability of HFD by introducing the normal cumulative distribution function and round function in dispersion entropy. Nevertheless, the parameter selection of DHFD can affect the complexity value. Therefore, an optimized dispersion HFD (ODHFD) is proposed, which solves the threshold selection problem of DHFD and can more effectively reflect the complexity of the signal. In addition, an optimized refined composite DHFD (ORCMDHFD) has been proposed, which can more comprehensively reflect the complexity information for the signal at multiple scales. The simulation experiment results show that DHFD has a smaller standard deviation than HFD when calculating white noise signal complexity, and DHFD have the least dependence on signal length compared to other metrics, as well as RCMDHFD has the best separability for simulated noise signals. Actual experiments have shown that ODHFD and ORCMDHFD is superior to other entropy and fractal dimension metrics in distinguishing ship radiated noise and mechanical fault signals, and has broad application prospects in the field of signal analysis.

A noise generative network to reduce the gap between simulation and measurement signals in mechanical fault diagnosis

Data-driven artificial intelligence models play an important role in mechanical fault diagnosis. Generally, it is difficult to collect relative complete fault samples, which limits the application of artificial intelligence models for complex mechanical systems. To address this issue, numerical model-based or called physical model-based fault sample generation methods attracted many attentions but still an open problem: the difference in fault samples between numerical simulation and measurement of a physical system needs to be well decreased. Therefore, a noise generative network (NGN) for mechanical fault classification is developed. First, the NGN is trained using the simulation and measurement normal samples. The simulation fault samples are further fed into the trained NGN to obtain more solid simulation fault samples with minor differences from measurements of mechanical systems with faults. Second, deep convolutional neural network is trained by solid simulation fault samples, and the test samples of unknown fault types will be finally recognized. Finally, validation experiments using rotation vector reducers, bearings, and rotors classified test samples with average accuracies of 99.5%, 95.7%, and 99.9%, respectively. They indicate that the proposed NGN model effectively reduces the difference between the simulation and measurement samples to lead more precision results for fault classification in mechanical systems.