Noise Modes Research Articles

High-resolution frequency domain decomposition for modal analysis of bridges using train-induced free-vibrations

Modal parameters are structural inherent characteristics that can be applied for revealing performance of railway bridges. Free vibration signals generated by a passage of train are commonly utilized to estimate the modal parameters of railway bridges due to their higher signal-to-noise ratios compared to random vibrations caused by ambient loads. However, since free vibration signals rapidly decay over time, the available free-vibration data is typically short-time. When using the fast Fourier transform-based spectral estimation method for modal identification from short-time vibration data, a phenomenon known as spectral leakage occurs, leading to miss-identification of some structural modes. In this study, the classical frequency domain decomposition (FDD) is improved for modal identification of railway bridges, in which the higher resolution auto-power spectral density (PSD) and cross-PSD functions are calculated through the autoregressive (AR) model-based method. The AR model-based method improves both the smoothness and resolution of the PSD functions compared to the fast Fourier transform technique. These AR model-based PSD functions are then employed in the FDD process to facilitate frequency and mode shape identification while avoiding spurious noise modes. The proposed eigenvalue fitting technique is subsequently utilized to estimate damping ratios. Numerical simulation data as well as vibration data from an actual bridge are analyzed to validate the proposed method, with a comparison made to the Welch’s PSD-based method. The results demonstrate that the modified FDD approach enables more effective identification of structural modes, even in the presence of closely-spaced modes.

Comparison of impulse noise energy distribution from two generations of parachute gun

The rocket ejection seat is an advanced life-saving device for fighter pilots. However, it can also produce damaging impulse noise during ejection, which could compromise the pilot’s hearing health, and the manufacturer must assess the noise damage. This study uses Variational Mode Decomposition (VMD) and Hilbert-Huang transform (HHT) to analyze energy-instantaneous distribution over time and frequency-instantaneous and energy contribution of different modes of impulse noise generated by the parachute gun in two generations of rocket ejection seats. The results demonstrate considerable disparities in the energy distribution of impulse noise generated by the parachute gun in the new generation seat compared to the old generation seat. The new generation seat has higher overall energy, more concentrated action time, and a higher energy contribution in the 1-10 kHz frequency range. These findings provide valuable data for the design of the new generation of noise protection equipment for pilots.

Acoustic mechanisms and tonal noise control of contra-rotating ducted fan by rotating speed regulation

The contra-rotating ducted fan (CRDF) involves more tonal noise modes compared with rotor/stator and single-rotor ducted fans. According to the present experiment on small-scale CRDF the dominant tone is concentrated at the harmonics of blade passing frequencies of front-rotor and rear-rotor as the modes of (m,0) and (0,n), as well as the modes (m,m) respectively. To investigate the flow mechanisms and sound propagation characteristics of these three types of tonal noise, unsteady three-dimensional numerical simulation was implemented under different rotational speed matching for CRDF and isolated rotor. Steady load and thickness noise were systematically analyzed using Hanson model. And the generation mechanisms of wake interaction noise and their contribution ratios were analyzed utilizing the duct sound model.It was indicated that tonal noise at the mode of (m,0) (0,n) of CRDF is primarily determined by the blade thickness and steady load. Compared with the single-rotor, the contra-rotating rotors could improve the load allocation between the front and rear rotor, resulting in the forward noise reduction with the constant thrust. Increasing the rear rotor speed enhances the contribution of potential flow interaction noise from the front rotor. Subsequently the front rotor load and wake interference are weakened, leading to noise reduction of approximately 0.3 dB for the rear rotor. However, the potential flow disturbance is intensified which causes noise increase by approximately 3.5 dB on the front rotor. But, increasing the front rotor speed will strengthen both the potential flow interference intensity on the pressure surface of the front rotor blade and the wake disturbance intensity on the leading edge of the rear rotor blade. This generates approximately 3 dB and 3.5 dB noise level increases for the front and rear rotors, respectively. As a result, appropriately raising the rear rotor speed can achieve a reasonable load allocation, and balance the potential flow interaction and wake interaction to realize the whole noise reduction without thrust loss.

Automated operational modal analysis for civil engineering structures with compressed measurements

Introducing compressive sensing (CS) to modal analysis enables modal identification approaches to process compressed measurements, which enhances the efficiency in both data transmission and analysis for long-term monitoring of civil engineering structures. Nevertheless, the existing methods normally require users’ judgment in mode selection, and thus they cannot realize automated modal analysis. To bridge this gap, this study proposes an automated CS-based operational modal analysis (OMA) method, namely Automated Sparse Decomposition (ASD). A mode selection strategy is proposed to gradually eliminate noise and false modes and thus realize automated modal identification based on compressed measurements. A search strategy with both extensive and intensive search stages is proposed to realize higher frequency search resolution with low computational costs. Additionally, an automated mode shape identification process is designed to enhance accuracy. To demonstrate the effectiveness of the proposed method, experimental data on an offshore wind turbine model and real-world monitoring data from an in-service cable-stayed bridge are analyzed. The results show that the identification accuracy of the proposed ASD is comparable to state-of-art CS-based methods. Due to its superior performance in both efficiency and accuracy, the proposed method has the potential to become a promising online modal analysis tool.

Impact of random spatial noise of a laser beam on the probability of fluence to exceed the threshold value

The analytical expressions of the probability for a two-dimensional random function (for example, fluence of a laser beam with noise) to exceed the threshold value over a given area have been derived. The probability is fully determined only by two parameters. The first one, the product of the beam area and the squared second normalized spectral moment, has the meaning of the equivalent number of noise modes and is determined only by the spectral properties of the noise. The second parameter, on the contrary, does not depend on the noise spectrum; it is determined only by the ratio of the standard deviation to the difference between the threshold and mean values of fluence. The numerical simulations are in a good agreement with the theory. The results are generalized to several cases of practical interest.

MinJoT: Multimodal infusion Joint Training for noise learning in text and multimodal classification problems

Amidst the critical role that high-quality labeled data plays in advancing machine learning, the persistence of noise within widely-used datasets remains a challenge. While noise learning has gained traction within machine learning, particularly in computer vision, its exploration in text and multimodal classification domains has lagged. Furthermore, a comprehensive comparison of noise learning techniques in text and multimodal classification has been lacking, partly due to variations in experimental noise settings across prior studies. Addressing these gaps, this research introduces a pioneering Multimodal Infusion Joint Training (MinJoT) framework featuring a novel co-regularized loss function that seamlessly integrates multimodal information during joint training. This framework notably excels in maintaining model robustness amidst noisy data environments. Adapting established noise learning methods from computer vision to text classification, the study conducts extensive experiments across five English and Chinese textual and multimodal datasets, involving four methods, five noise modes, and seven noise rates. Critically, this work challenges the implicit assumption that widely-used datasets are devoid of noise, revealing that these datasets indeed encompass noise levels ranging from 0.61% to 15.77% which is defined as intrinsic noise in this study. For the first time, the study investigates the impact of intrinsic noise on model performance, categorizing it into distinct levels of ambiguity. To facilitate accurate method comparison, a new dataset, Golden-Chnsenticorp (G-Chnsenticorp), is introduced, carefully crafted to be free of intrinsic noise. This research establishes the inaugural noise learning benchmark for text classification, while simultaneously pioneering the first noise learning framework tailored for multimodal sentiment classification. Through these contributions, the study advances the understanding of noise learning in text and multimodal contexts, providing a novel framework, benchmarks, and insights to propel the field forward.

Detection and time–frequency analysis of multiple plant-wide oscillations using adaptive multivariate intrinsic chirp component decomposition

Analyzing plant-wide oscillations is a challenging task owing to the presence of noise, nonstationarity, and multiple modes in a process control system. Multivariate intrinsic chirp component decomposition (MICCD) is a novel powerful tool for multivariate signal processing. Nevertheless, MICCD requires users to provide component number in advance, which restricts its adaptability. This study proposes an adaptive MICCD (AMICCD) that can adaptively determine the component number by utilizing the permutation entropy of instantaneous frequency. An AMICCD-based time–frequency analysis framework is presented to detect and characterize the multiple plant-wide oscillations. Compared to the latest methods, such as multivariate empirical mode decomposition and multivariate intrinsic time-scale decomposition, the proposed method can process not only single/multiple plant-wide oscillations, but also time-invariant/time-varying plant-wide oscillations. In particular, the proposed method can directly provide the time–frequency curves of multiple plant-wide oscillations, which have not been achieved by the state-of-the-art techniques. Finally, the effectiveness and advantages of the proposed approach are demonstrated on a wide variety of simulations and industrial cases.

Variational Mode Decomposition for Raman Spectral Denoising.

As a fast and non-destructive spectroscopic analysis technique, Raman spectroscopy has been widely applied in chemistry. However, noise is usually unavoidable in Raman spectra. Hence, denoising is an important step before Raman spectral analysis. A novel spectral denoising method based on variational mode decomposition (VMD) was introduced to solve the above problem. The spectrum is decomposed into a series of modes (uk) by VMD. Then, the high-frequency noise modes are removed and the remaining modes are reconstructed to obtain the denoised spectrum. The proposed method was verified by two artificial noised signals and two Raman spectra of inorganic materials, i.e., MnCo ISAs/CN and Fe-NCNT. For comparison, empirical mode decomposition (EMD), Savitzky-Golay (SG) smoothing, and discrete wavelet transformation (DWT) are also investigated. At the same time, signal-to-noise ratio (SNR) was introduced as evaluation indicators to verify the performance of the proposed method. The results show that compared with EMD, VMD can significantly improve mode mixing and the endpoint effect. Moreover, the Raman spectrum by VMD denoising is more excellent than that of EMD, SG smoothing and DWT in terms of visualization and SNR. For the small sharp peaks, some information is lost after denoising by EMD, SG smoothing, DWT and VMD while VMD loses fewest information. Therefore, VMD may be an alternative method for Raman spectral denoising.

An adaptive linear-mode decomposition for effective separation of linear and nonlinear seismic events, ground roll, and random noise

Ground roll and random noise usually mask primary reflections in land seismic data. Different sets of signal processing methods are used to suppress these two noises based on statistical and/or transformation filtering. Among these methods, linear-mode decomposition (LMD) decomposes linear and nonlinear seismic events into amplitude-frequency modulated modes using the Wiener filter. Different combinations of these decomposed linear modes then can be used to represent different seismic events. However, LMD requires predefining the level of decomposition that must be selected carefully to avoid suboptimal binning, which can influence the fidelity of the decomposed seismic modes. To that end, we introduce an adaptive LMD (ALMD) that optimally separates seismic events, ground roll, and random noise. ALMD uses the correlation between the decomposed modes and the input data to determine the decomposition level. Consequently, an optimum decomposition divides the data into linear modes with minimum mixing. In addition, unlike conventional ground roll suppression methods, ALMD does not require estimating the slope or the frequency bandwidth of the ground roll. Moreover, ALMD automates the random noise segregation by separating modes as the signal, noise, and mixed modes, based on the permutation entropy and kurtosis criteria. ALMD iteratively decomposes mixed modes with remnant random noise until a signal or noise criterion is met. Using synthetic and real data examples, we demonstrate that the proposed ALMD is an effective method for separating desired linear and nonlinear events, unwanted ground roll energy, and random noise from the seismic data.

A Static Reduced-Order Multiple-Model Adaptive Estimator for Noise Identification

A reduced-order multiple-model (MM) estimator for noise identification in dynamic stochastic systems is developed. The unknown noise statistics are assumed to be static. While a standard static MM estimator does not grow exponentially over time, its computational complexity grows exponentially with the number of modes. The proposed algorithm reduces the computational complexity of MM estimation from exponential to polynomial by constructing a significantly smaller set of mode models which are updated every time step. It is assumed that the constructed mode models do not change significantly between time steps, which in turn holds if the smoothness of the mode probabilities is guaranteed. It is shown that in the case where there is “enough” statistical distinction between the noise modes, the proposed reduced-order MM estimator converges to the standard MM estimator. The proposed reduced-order MM estimator is evaluated using Monte Carlo simulations, showing that it performs nearly similar to the standard MM estimator with a fraction of its complexity. The numerical example shows less than a 2% increase in the root mean-squared errors (RMSEs) of the reduced-order MM estimator from the standard one, while the reduction in the number of filters in the reduced-order MM estimator is 300%. To further validate the proposed filter, experimental results are presented of an unmanned aerial vehicle (UAV) navigating with terrestrial signals of opportunity. Opportunistic navigation serves a relevant application for MM-based estimation, as system parameters, namely the statistics of the clock error dynamics of opportunistic sources, are unknown and must be adaptively estimated. The experimental results show a UAV navigating for more than 5 minutes over a trajectory of more than 3 km, achieving a final position error of 6.21 m obtained using the standard MM estimator versus a final position error of 6.25 m obtained using the proposed reduced-order MM estimator. A standard extended Kalman filter (EKF) was implemented for comparative analysis, showing a final error of 40.03 m. In the experiments, the reduced-order MM estimator was implemented with 16 filters, while the standard MM was implemented with 256 filters.

Broadband Acoustic Modal Identification by Array Nonsynchronous Measurements with One Reference

This paper proposes a broadband acoustic modal identification method based on nonsynchronous measurements from a microphone array, which overcomes the acoustic modal aliasing problem caused by a limited number of microphones. In the proposed method, the number of measurement points is augmented by sequentially rotating a circular microphone array that is circumferentially mounted in the wall of the compressor duct. This technique employs only one reference microphone to enhance the robustness of the cross-spectral matrix completion for nonsynchronous measurements. The fast iterative shrinkage threshold algorithm is adopted to iteratively complete the cross-spectral matrix of all measurement points for acoustic modal identification. The in-duct acoustic modal coefficients of the broadband noise are then determined using the least-squares method with the sound pressure vector recovered from the completed cross-spectral matrix. The accuracy of the cross-spectral matrix and the identified acoustic modals is verified through numerical simulations. The proposed method is validated by experimental results from an axial flow compressor. The results show that it can effectively suppress the modal aliasing problem and identify the azimuthal modes of compressor broadband noise.

A novel structural damage detection method using a hybrid IDE–BP model

Recently, artificial intelligence has been widely applied in the field of structural damage detection. In this study, a back-propagation neural network (BPNN) hybrid with an improved differential evolution algorithm (IDE) is proposed to advance the development of structural damage detection. Generally, most engineering structures’ dynamic characteristics can be directly obtained by modal analysis, and the corresponding vibration features can provide information on structural damage. Thus, based on the results of modal analysis, artificial intelligent methods such as BP neural network find their way to locate the damage and quantify the damage severity. However, the existing research shows that the original BP has insufficient accuracy in structural damage detection, especially cannot effectively deal with complex structural damage. In this study, an improved differential evolution algorithm is proposed, introducing a group sampling strategy and an adaptive mutation strategy. Then, the proposed IDE and BP neural networks are hybridized into IDE–BP to promote the neural network’s data classification and regression ability. Subsequently, based on the IDE–BP model, a method for detecting the location and severity of multi-damage scenarios is established. The decision tree algorithm (DT) is utilized to identify the number of damaged elements. To verify the effectiveness of the proposed damage detection method, the IDE–BP model is applied to the damage detection for a steel pipe and a cantilever plate. To study the anti-noise ability and robustness of the IDE–BP model, the noise and incomplete modes are introduced. Compared with the original BP, SVM, KNN, PSO–BP,​ and DE–BP, the numerical results show that the damage detection method based on the IDE–BP model performs better for all single and multiple damage scenarios.

Personal noise exposure during daily commutes and subjectively reported stress: A trip stage level analysis of MobiliSense data

BackgroundThe auditory and non-auditory health effects of noise have long been established in the literature but previous studies have mainly been in the context of occupational risk. Recent research suggests that daily noise exposures occurring during social activities and commute may be associated with higher psychological stress. Our aim was to explore the association between modes of transportation and noise on the one hand and noise exposures and reported stress on the other hand. Although existing literature shows that noise exposure is especially high during commutes, studies on the topic have been sparse due to cost and logistic constraints; furthermore, sample sizes have been small. MethodsThe present study uses data collected on the daily commutes of 253 participants of the Mobilisense cohort study between 2018 and 2020. Personal dosimeters were used to measure noise exposure by frequency bands over a period of 4 days, resulting in a sample of 7800 trip stage windows. Participants reported trip stress levels during an a posteriori phone mobility survey on a scale from 1 (no stress) to 7 (significantly stressful conditions). Modes of transportation (metro, car, walking, etc.) were collected from the same mobility survey based on Global Positioning System (GPS) receivers. ResultsWhile all transport modes resulted in higher exposure to low frequency noise compared to walking, all modes but tramway and driving or being the passenger of a car were associated with an increased exposure to high frequency noise. The LAeq noise indicator (overall noise) was associated with reported stress: for every 10 dB(A) increase in LAeq, individuals reported experiencing 1.118 times (95% confidence interval: 1.067, 1.172) higher levels of stress. Multiple noise indicator models did not show evidence that specific frequency components were associated with stress beyond overall noise. ConclusionOur findings suggest that noise exposure during commutes vary according to modes of transportation. Given that noise exposure resulted in higher reported levels of stress, future research should examine transportation noise effects on physiological variables.

A Video Splicing Forgery Detection and Localization Algorithm Based on Sensor Pattern Noise

Video splicing forgery is a common object-based intra-frame forgery operation. It refers to copying some regions, usually moving foreground objects, from one video to another. The splicing video usually contains two different modes of camera sensor pattern noise (SPN). Therefore, the SPN, which is called a camera fingerprint, can be used to detect video splicing operations. The paper proposes a video splicing detection and localization scheme based on SPN, which consists of detecting moving objects, estimating reference SPN, and calculating signed peak-to-correlation energy (SPCE). Firstly, foreground objects of the frame are extracted, and then, reference SPN are trained using frames without foreground objects. Finally, the SPCE is calculated at the block level to distinguish forged objects from normal objects. Experimental results demonstrate that the method can accurately locate the tampered area and has higher detection accuracy. In terms of accuracy and F1-score, our method achieves 0.914 and 0.912, respectively.

Spin-noise spectroscopy of a spin-one system

The spin-noise mechanisms in a spin-1 system are theoretically and experimentally investigated in detail. Eight different independent spin degrees of freedom are isolated, leading to spin-noise signals at the Larmor frequency or its second harmonic in the presence of a magnetic field. The signatures of these different modes of the detuned probe light beam polarization fluctuations, observed either as Faraday rotation noise or as ellipticity noise, are shown to depend dramatically on the probed transition. In particular, depending on the Zeeman structure of the upper level of the transitions, the different noise modes can be visible or not, and their dependence on the probe polarization can be completely modified. Those predictions are successfully compared with measurements of spin-noise signals using three different transitions of an ensemble of metastable $^{4}\mathrm{He}$ at room temperature.

Application and comparison of dynamic mode decomposition methods in the tip leakage cavitation of a hydrofoil case

The cavitation of the tip leakage vortex (TLV) induced by tip leakage has always been a difficult problem faced by turbomachinery, and its flow structure is complex and diverse. How to accurately extract the main structures that affect the cavitating flow of the TLV from the two-phase flow field is a key problem. In this study, the main mode extraction and low order mode reconstruction accuracy of the cavitation flow field of TLV downstream of National Advisory Committee for Aeronautics (NACA)0009 hydrofoil by two dynamic mode decomposition (DMD) methods are compared. The research shows that the main modes extracted by the standard DMD method contain a large number of noise modes, while the sparsity-promoting DMD eliminates the noise modes, showing obvious advantages in the reconstruction accuracy of the velocity field. The characteristics of cavitation signals are analyzed, and the cavitation signals are divided into four categories, which explains the reason why DMD methods have low reconstruction accuracy in cavitation. This study provides a theoretical basis and strong guarantee for the extraction of mode decomposition characteristics of the two-phase flow field. This is of great significance for accelerating the prediction of multiphase flow fields based on intelligent flow pattern learning in the future. Meanwhile, it also provides a new method and road for the introduction of artificial intelligence technology in future scientific research.

A sensitive single-end DC line fault detection method for MMC-HVDC grids using reactor voltage ratio

The fast, sensitive, and reliable primary protection method can ensure the continuous and reliable operation of the modular multilevel converter (MMC)–based HVDC grid during dc faults. This paper proposes a fault detection method based on the ratio of voltage integrals to improve the single-end method sensitivity to high-resistance faults. First, the reactor line-mode transient voltage caused by the initial traveling wave (TW) is analyzed in the fault component network. The exponential term of the voltage is independent of the fault resistance and can be reflected by the proposed voltage integral ratio. Next, the influence of subsequent TWs on the integral is analyzed. The partition time, selected by the mathematical morphology, is proposed to divide the voltage sampling data into two parts. Finally, the criterion using the ratio of the two-part voltage integral is proposed to detect internal faults. The ± 500 kV bipolar MMC-HVDC grid built on PSCAD/EMTDC validates that the proposed method can fast identify internal faults, and the protected zone covers the whole line length. Smulation cases analyze the influence of the reactor and sampling frequencies on the proposed method. Moreover, the method is sensitive to high-resistance faults and can work under white noise and different wiring modes.

Recovering cutting tool modal parameters from randomly sampled signals using compressed sensing

A change in the modal parameters of cutting tools could signal tool wear, tool breakage, or other instabilities. The cutting process must be continuously monitored using vibration signals to detect such changes. Since tools vibrate with frequencies of up to a few kHz, continuous monitoring requires sampling at rates of tens of kHz to respect the Nyquist limit. Processing and storing such large data for decision making is cumbersome. To address this issue, this paper discusses the use of a compressed sensing framework that enables non-uniform random sampling at rates below the Nyquist limit. For cutting tools, we show for the first time using synthesized data that it is possible to reconstruct original signals from as few as 1% of the original data. We numerically test the method to characterize the influence of damping, noise, and multiple modes. Recovered modal parameters from the reconstructed signal agree with signals sampled properly.

Uncertainty quantification in operational modal analysis of time-varying structures based on time-dependent autoregressive moving average model

The identified modal parameters in Operational Modal Analysis (OMA) are always subject to statistical uncertainties from many sources and the uncertainty is also important to assess the quality of the identified results. This study proposes an uncertainty quantification method in OMA for time-varying structures based on the Functional Series Time-dependent AutoRegressive Moving Average (FS-TARMA) model. A multi-stage pseudo-linear optimization scheme is first adopted to obtain the approximate Maximum Likelihood Estimates (MLEs) of FS-TARMA model parameters and the covariance is computed as the inverse Hessian matrix of the Negative Log-Likelihood Function (NLLF) at the MLEs. Finally, the approximate closed-form covariance of modal parameters is derived by the first-order sensitivity method. The proposed method is verified by two numerical examples and an experimental beam. Furthermore, the ability of the uncertainty to extinguish the spurious modes caused by colored noise excitation and the noise modes caused by measurement noise or inappropriate model structure is also demonstrated in these examples.

Noise-resilient edge modes on a chain of superconducting qubits.

Inherent symmetry of a quantum system may protect its otherwise fragile states. Leveraging such protection requires testing its robustness against uncontrolled environmental interactions. Using 47 superconducting qubits, we implement the one-dimensional kicked Ising model, which exhibits nonlocal Majorana edge modes (MEMs) with [Formula: see text] parity symmetry. We find that any multiqubit Pauli operator overlapping with the MEMs exhibits a uniform late-time decay rate comparable to single-qubit relaxation rates, irrespective of its size or composition. This characteristic allows us to accurately reconstruct the exponentially localized spatial profiles of the MEMs. Furthermore, the MEMs are found to be resilient against certain symmetry-breaking noise owing to a prethermalization mechanism. Our work elucidates the complex interplay between noise and symmetry-protected edge modes in a solid-state environment.