Component Mode Research Articles

Research on detection and protection technology of DC residual current

Abstract To realize that the residual current device can accurately and quickly detect the fault when the fluctuating DC residual current occurs in the system and improve the system’s safety, a DC residual current detection and protection method based on VMD was proposed. Particle Swarm Optimization (PSO) was used to optimize the penalty factor and the number of decomposition layers of VMD. The minimum envelope entropy of the residual current signal is used as the fitness function to obtain the optimal combination. The criterion of fault occurrence is established based on the optimal modal component mutation characteristic of the residual current decomposed by VMD. Experiments show that this method can identify faults quickly and effectively when pulsating DC residual current is generated in the system.

Traveling wave mode analysis of a coherence collapse regime semiconductor laser with optical feedback

A highly developed traveling wave model for a semiconductor laser system supports sophisticated mode analysis of the coherence collapse regime in semiconductor lasers with delayed optical feedback. The concept of instantaneous optical modes is used. Time-frequency representations of chaotic trajectories are constructed and interpreted. This involves synthesizing the calculated optical modes with their corresponding steady states, analysis of the mode driving and coupling sources, and field expansion into modal components. The results support detailed physical interpretation of the optical and radiofrequency spectra throughout the coherence collapse regime. This includes simulation results for the transition out of coherence collapse at high optical feedback. Also key frequencies observed in the radiofrequency spectrum of the chaotic output are linked to the modes that mix to generate them.

A Deep Learning PM2.5 Hybrid Prediction Model Based on Clustering–Secondary Decomposition Strategy

Accurate prediction of PM2.5 concentration is important for pollution control, public health, and ecological protection. However, due to the nonlinear nature of PM2.5 data, the accuracy of existing methods suffers and performs poorly in both short-term and long-term predictions. In this study, a deep learning hybrid prediction model based on clustering and quadratic decomposition is proposed. The model utilizes the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose the PM2.5 sequences into multiple intrinsic modal function components (IMFs), and clusters and re-fuses the subsequences with similar complexity by permutation entropy (PE) and K-means clustering. For the fused high-frequency sequences, a secondary decomposition is performed using the whale optimization algorithm (WOA) optimized variational modal decomposition (VMD). Finally, the nonlinear and temporal features are captured for prediction using the long- and short-term memory neural network (LSTM). Experiments show that this proposed model exhibits good stability and generalization ability. It does not only make accurate predictions in the short term, but also captures the trends in the long-term prediction. There is a significant performance improvement over the baseline models. Further comparisons with existing models outperform the current state-of-the-art models.

Unveiling non-monochromatic modes and nonlinearity in Piet Hein quantum semiconductor waveguides

We investigate the propagation characteristics of transverse electric (TE) and transverse magnetic (TM) modes in a semiconductor quantum plasma-filled coaxial waveguide with a Piet Hein cross-section. The unique geometry of the Piet Hein cross-section offers intriguing possibilities for tailoring modal properties and exploiting novel nonlinear phenomena. Using analytical and numerical methods, we unveil the non-monochromatic behaviour of TE and TM modes, characterized by distinct peaks and troughs in their field components. The Ez\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$E_{z}$$\\end{document} component of the TM mode exhibits a suppressed field within the central core and higher concentration in the cladding region, potentially minimizing energy loss within the waveguide. The Eρ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$E_{\\rho }$$\\end{document} component exhibits pronounced oscillations near the waveguide boundaries, suggesting constructive and destructive interference patterns. The Eξ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$E_{\\xi }$$\\end{document} component displays a non-monotonic behaviour with multiple pits and bumps, highlighting the interplay between the TE mode, the Piet Hein geometry, and the semiconductor quantum plasma properties. The Bρ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$B_{\\rho }$$\\end{document} component of the TM mode exhibits a non-monochromatic behaviour with multiple maxima and minima, attributed to the complex interactions between propagating waves with different phase shifts. The Bξ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$B_{\\xi }$$\\end{document} component exhibits a non-uniform distribution with distinct pits and bumps, with a resurgence towards the waveguide edges potentially due to higher-order mode contributions and local field enhancement effects. Our findings pave the way for the development of novel photonic devices with enhanced functionalities based on Piet Hein semiconductor quantum plasma waveguides.

A New Algorithm for Predicting Dam Deformation Using Grey Wolf-Optimized Variational Mode Long Short-Term Neural Network

To solve the problems of difficult-to-model parameter selections, useful-signal extraction and improper-signal decomposition in nonlinear, non-stationary dam displacement time series prediction methods, we propose a new predictive model for grey wolf optimization and variational mode decomposition and long short-term memory (GVLSTM). Firstly, we used the grey wolf optimization (GWO) algorithm to optimize the parameters of variable mode decomposition (VMD), obtaining the optimal parameter combination. Secondly, we used multiscale permutation entropy (MPE) as a standard to select signal screening, determining and recon-structing the effective modal components. Finally, the long short-term memory neural network (LSTM) was used to learn the dam deformation characteristics. The result shows that the GVLSTM model can effectively reduce the estimation deviation of the prediction model. Compared with VMDGRU and VMDANN, the average RMSE and MAE value of each station is increased by 19.11%~28.58% and 27.66%~29.63%, respectively. We used determination (R2) coefficient to judge the performance of the prediction model, and the value of R2 was 0.95~0.97, indicating that our method has good performance in predicting dam deformation. The proposed method has outstanding advantages of high accuracy, reliability, and stability for dam deformation prediction.

Simulation and experimental analysis of traveling wave resonance of flexible spiral bevel gear system

Considering the traveling wave resonance (TWR) phenomenon of the spiral bevel gear system in the aero engine accessory casing, the flexible spiral bevel gear model is established by three-dimensional (3D) shell element and beam element. The dynamic model of flexible bevel gear system considering the change of meshing teeth pairs in the operating process is established by component mode synthesis (CMS) method and rotational modal projection method. The proposed model is verified by finite element (FE) solid model and experiment test results. On this basis, the TWR phenomenon of the bevel gear system is studied in combination with dynamic response and vibration stress experimental test. The results show that the bevel gear system has 3 nodal diameter resonance speeds within the actual operating speed range, which are one 1st nodal diameter resonance of pinion and two 1st nodal diameter resonance of gear. When the bevel gear system operates at TWR state, the amplitude corresponding to fm ± m·fr (fm is meshing frequency, m is nodal diameter number, fr is shaft rotational frequency) is larger than that corresponding to fm in the Fast Fourier transform (FFT) spectrum of the of the gear foundation vibration stress, and the vibration stress cloud map of the gear foundation shows the corresponding nodal diameter vibration shape. The research results provide a theoretical basis for feature recognition and influence law evaluation of TWR of bevel gear system.

An extension of the approximate component mode synthesis method to the heterogeneous Helmholtz equation

Abstract In this work, we propose and analyze an extension of the approximate component mode synthesis (ACMS) method to the two-dimensional heterogeneous Helmholtz equation. The ACMS method has originally been introduced by Hetmaniuk and Lehoucq as a multiscale method to solve elliptic partial differential equations. The ACMS method uses a domain decomposition to separate the numerical approximation by splitting the variational problem into two independent parts: local Helmholtz problems and a global interface problem. While the former are naturally local and decoupled such that they can be easily solved in parallel, the latter requires the construction of suitable local basis functions relying on local eigenmodes and suitable extensions. We carry out a full error analysis of this approach focusing on the case where the domain decomposition is kept fixed, but the number of eigenfunctions is increased. The theoretical results in this work are supported by numerical experiments verifying algebraic convergence for the method. In certain, practically relevant cases, even super-algebraic convergence for the local Helmholtz problems can be achieved without oversampling.

Contractor-oriented optimization for wind farm maintenance decision-making considering partial monitoring information and degraded working efficiency

Maintenance strategies play a crucial role in the development and success of wind farm management, as they have a significant impact on the profitability of wind generation. The current maintenance decision-making process focuses mostly on the wind farm owner, while ignoring the benefits from the perspective of maintenance contractor. To bridge this gap, a novel contractor-oriented maintenance strategy is developed for both onshore and offshore wind farms to maximize profits for the maintenance contractor. Compared to the reported works, two main contributions are made. First, it is the first time that the maintenance strategy of a wind farm is designed from the perspective of the maintenance contractor, with emphasis on an accurate profit assessment. For this purpose, by utilizing remaining useful life (RUL) information from monitorable and non-monitorable components, a degradation-related efficiency model is constructed to quantify the loss of turbine efficiency due to degraded components and to incorporate it into the maintenance decision-making process. Secondly, various identifiable failure modes of the turbine components are investigated and simulated, as the contractor is more concerned about the maintenance costs under different failure modes. A case study using the dataset collected from real wind farms is provided to demonstrate and validate the proposed maintenance optimization method. The results indicate the proposed method can contribute to the maximum profits of the contractor while satisfying the requirements of the wind farm owner.

Anticrossing and Mode Coupling in Bent All-Glass Leakage Channel Microstructured Optical Fibers with Large Mode Area

This paper presents the results of a detailed theoretical study of the bending properties of original all-glass leakage channel microstructured optical fibers (LC MOFs) over a bending radius range from 3 cm to 11 cm. These LC MOFs contain two layers of fluorine-doped silica glass elements with reduced refractive index, different diameters, and different distances between them. We determined the spatial distributions of the electric field components of different modes in addition to the usual parameters such as effective refractive indices, bending losses, and spatial intensity distributions. A detailed analysis showed that three modes for each polarization have to be considered to correctly calculate the bending losses. Two pairs of these three modes couple in two distinct bending radius ranges, specifically near 3.68 cm and near 5.95 cm, and the mode coupling in these pairs is resonant. The resulting bending losses of the LC MOF for two polarizations are very close to each other and have two maxima at bending radii of 3.68 cm and 5.95 cm. However, the nature of these maxima is not resonant; they are caused by the combined influence of all three modes, each of which has specific dependencies of losses and other parameters on the bending radius that exhibit quasi-resonant behavior near the corresponding bending radii.

A Method for Single-Phase Ground Fault Section Location in Distribution Networks Based on Improved Empirical Wavelet Transform and Graph Isomorphic Networks

When single-phase ground faults occur in distribution systems, the fault characteristics of zero-sequence current signals are not prominent. They are quickly submerged in noise, leading to difficulties in fault section location. This paper proposes a method for fault section location in distribution networks based on improved empirical wavelet transform (IEWT) and GINs to address this issue. Firstly, based on kurtosis, EWT is optimized using the N-point search method to decompose the zero-sequence current signal into modal components. Noise is filtered out through weighted permutation entropy (WPE), and signal reconstruction is performed to obtain the denoised zero-sequence current signal. Subsequently, GINs are employed for graph classification tasks. According to the topology of the distribution network, the corresponding graph is constructed as the input to the GIN. The denoised zero-sequence current signal is the node input for the GIN. The GIN autonomously explores the features of each graph structure to achieve fault section location. The experimental results demonstrate that this method has strong noise resistance, with a fault section location accuracy of up to 99.95%, effectively completing fault section location in distribution networks.

Application of three-dimensional MHD equilibrium calculation coupled with plasma response to island divertor experiments on J-TEXT

Three-dimensional (3D) equilibrium calculations, including the plasma rotation shielding effect to resonant magnetic perturbations (RMPs) produced by the island divertor (ID) coils, were carried out using the HINT and MARS-F codes on J-TEXT. Validation of 3D equilibrium calculations with experimental observations demonstrates that the shielding effect will prevent the penetration of the edge m/n = 3/1 mode component when the ID coil current is 4 kA, while change the size of magnetic islands once the current exceeds the penetration threshold. This indicates that equilibrium calculations including the plasma rotation shielding effect to RMPs can lead to better agreements with experimental observations compared to the vacuum approximation method. Additionally, the magnetic topology at the boundary undergoes changes, impacting the interaction between the plasma and the target plate. These results may be important in understanding RMP effects on edge transport and magnetohydrodynamic (MHD) instability control, as well as divertor heat and particle flux distribution control.

Force mechanism analysis of composite microbial dust suppressants based on extracellular polymeric substances (EPS) mode components

As an important potential dust suppression method, the slow onset time is one of the key factors that restrict the effect of microbial dust suppressant. In the early stage, we have confirmed that extracellular polymeric substances (EPS) can improve the dust suppression effect by wetting coal dust and increasing Ca2+ nucleation sites. Therefore, in this study, chitosan (CTS) and bovine serum albumin (BSA) in different ratios (CTS: BSA = 1:1, 1:2, 2:1) as model molecules of EPS were combined with Bacillus subtilis to prepare efficient and fast microbial dust suppressants. Furthermore, the interaction forces were analyzed through molecular dynamics simulation. Results showed that adding CTS and BSA would improve the dust suppression effect, and the dust suppression effect was the best when the ratio of CTS: BSA was 1:2. In addition, the contact angle decreased as the BSA content increased. The Fourier transform infrared spectroscopy (FTIR) results showed that when the ratio of CTS to BSA was 1:2, the dust suppressants were easier to interact with coal dust by the key functional groups and form calcite type CaCO3. The molecular dynamics simulation results showed that the main interaction was Van der Waals force. In addition, the interaction force was strongest when CTS: BSA was 1:2, increasing by 137% compared with the microbial dust suppressants without CTS or BSA. This study provides theoretical support for the development of efficient and rapid microbial dust suppressants.

Efficient compression of reduced impedance matrices for trimmed structural models: a novel methodology and industrial validation

In response to increasingly strict vibration and noise regulations, software frameworks such as Nastran and Actran have stepped up to the challenge, providing sophisticated solutions to efficiently model damping treatments within numerical methods. One of the available techniques is the representation of trim components as frequency-dependent reduced impedance matrices (RIM) in a frequency response analysis of fully trimmed models. While RIM enables coupling between physical or modal-based components, challenges arise due to large dense matrices. Each matrix is reduced either on physical coupling DOFs or modes of vibration of the component and stored on disk. Since each matrix is dense and large in industrial models, it leads to storage and efficiency issues related to computational and input/output (I/O) operations. This paper introduces a methodology to compress RIMs, particularly for modal components, by selecting the most contributing modes to represent the coupling relationship of each surface. Numerical results on an industrial car body model demonstrate that this approach effectively eliminates low-effect contributions, minimizes storage requirements, and reduces computational time. General Motors' involvement in validating the method adds real-world applicability and reliability to the findings, enhancing its practical engineering value.

Dynamic substructuring for visco-elastic materials using a POD-based model reduction method: application to laminated panels

Dynamic substructuring is now a well established and powerful tool for engineers used to analyze the dynamic response of mechanical systems. The technique is particularly favored for the simulation of mechanical vibrations allowing different aspects to be studied. It consists in splitting the global structure into several substructures from which reduced models are built. In Finite Element software, these reduced models, also called superelements, are generally built using the concept of Component Mode Synthesis. Difficulties arise when structures made of frequency-dependent materials are considered since classical modal reduction algorithms are inapplicable as they are because of the frequency-dependence of the stiffness matrix. Based on the Balanced Truncation approach, a novel methodology for the construction of a superelement for the dynamic analysis of elastic structures made with viscoelastic materials is presented. The methodology takes advantage of the Golla-Hughes-McTavish rheological model (GHM) before reducing the system via the Balanced Proper Orthogonal Decomposition (BPOD). The coupling of the superelement with a host structure is achieved via the use of Lagrange multipliers. The reduced system takes the form of a constant matrix of very small size compared to the original FE model. The method is applied to laminated panels such as an automobile windscreen.

Study on increasing load capacity of wooden arch bridge by CFRP strengthening: experimental and numerical Verification

The wooden arch corridor bridge is a typical representative of Chinese wooden bridges, holding significant historical research value. Currently, these bridges face issues of severe component deformation and insufficient load-bearing capacity. To address these problems, this study employs CFRP reinforcement on the components of wooden arch corridor bridges to reduce deformation and enhance load-bearing capacity. Experimental research on CFRP reinforcement has yielded the elastic modulus of the bonding interface. Given the lack of an accurate numerical model for wooden arch corridor bridges, this study establishes a precise numerical model by setting parameters based on load test data from wooden arch corridor bridges. The elastic modulus obtained from the experiments is input into the numerical model for analysis. The results indicate that CFRP exhibits excellent reinforcement performance, with the load-bearing capacity of the reinforced damaged components still reaching 75%–85% of their original capacity, while the load-bearing capacity of the reinforced undamaged components increases to 130%–140% of their original capacity. The failure modes of the CFRP-reinforced wooden components suggest that allowing for some gaps in the bonding of CFRP can enhance overall ductility. The application of CFRP to wooden arch corridor bridges also demonstrates favorable reinforcement effects, increasing the load-bearing capacity of the arch surface by approximately 20%, thereby providing a theoretical basis for the reinforcement of wooden arch bridge frameworks.

Application of SPNGO-VMD-SVM in rolling bearing fault diagnosis

Abstract Traditional bearing fault feature extraction and fault classification methods have low recognition accuracy and limited recognition capability in noisy environments. To address this problem, this paper proposes an improved northern eagle algorithm (SPNGO) to optimize the variational modal decomposition (VMD) and support vector machine (SVM) to achieve bearing fault diagnosis. Firstly, to overcome the disadvantages of NGO, such as easy fall into local optimal solutions and slow convergence speed, the Sine Cosine Strategy (SCA) and Position Optimisation Search Algorithm (POS) are introduced to optimize the NGO, and the superiority of the SPNGO algorithm is proved through the comparison of different algorithms. Then, SPNGO-VMD is used to adaptively decompose the vibration signals of faulty bearings and generate multiple modal components IMF. The effective IMF components are screened based on the craggy principle to reconstruct the signals. Finally, the reconstructed feature signals are input into SPNGO-SVM for fault classification and compared with other fault diagnosis models. The study results show that the fault diagnosis accuracy of SPNGO-VMD-SVM can reach 96.67%, which can effectively improve the accuracy of bearing fault diagnosis.

Modal gain characteristics of few-mode erbium-doped fiber amplifiers with pump mode beating

Using the linear polarization (LP) mode decomposition method, we theoretically analyze the beat effect between the same polarization components of the vector pump modes and propose a pump beat model of few-mode erbium-doped fiber amplifiers (FM-EDFAs). The relationship of the overlap factor with the signal gain in the beat model is verified by the amplification of LP01 and LP11 modes for the first time, that is, the differential modal gain (DMG) is dependent on the integral of the overlap factor difference over the EDF length. We also simulate the effect of pump mode beating on signal amplification: the modal gain varies periodically with the pump mode phase, and the beat length can affect the fluctuation period and amplitude of the DMG. The DMG can further be reduced by controlling the initial phases of the vector modes and properly designing the beat length of the FM-EDF. The similar process can expand to more signal modes, such as the simultaneous amplification of LP01x, LP11ex, LP11oy, LP21ex and LP21oy modes. The LP mode decomposition method is also applied to the beat effect between signal modes or the amplification of orbital angular momentum (OAM) modes. One can expect to implement an experiment on the FM-EDFA amplification with pump mode beating by optimally designing the FM-EDF structure, using a properly narrow linewidth pump laser and precisely controlling the pump mode coherence.

Research on Signal Noise Reduction and Leakage Localization in Urban Water Supply Pipelines Based on Northern Goshawk Optimization.

In order to enhance the accuracy and adaptability of urban water supply pipeline leak localization, based on the Northern Goshawk Optimization, a novel joint denoising method is proposed in this paper to reduce noise in negative pressure wave signals caused by leaks. Firstly, the Northern Goshawk Optimization optimizes the decomposition levels and penalty factors of Variational Mode Decomposition, and obtains their optimal combination. Subsequently, the optimized parameters are used to decompose the pressure signals into modal components, and the effective components and noise components are distinguished according to the correlation coefficients. Then, an optimized wavelet thresholding method is applied to the selected effective components for secondary denoising. Finally, the signal components that have been denoised twice are reconstructed with the effective signal components, and the denoised negative pressure wave signals are obtained. Simulation experiments demonstrate that compared to wavelet transforms and Empirical Mode Decomposition, our method achieves the highest signal-to-noise ratio improvement of 12.23 dB and normalized cross correlation of 0.991. It effectively preserves useful leak information in the signal while suppressing noise, laying a solid foundation for improving leak localization accuracy. After several leak simulation tests on the leakage simulation test platform, the test results verify the effectiveness of the proposed method. The minimum relative error of the leakage localization is 0.29%, and an average relative error is 1.64%, achieving accurate leakage localization.

Rotating machinery early fault detection integrating variational mode decomposition and multiscale singular value decomposition

Abstract Security and reliability are important issues that must be paid attention to during the operation of rotating machinery. If defects can be found in the early stage, there will be enough time to take maintenance measures and realize the stable operation of equipment. However, the presence of noise, shaft rotation signals, gear meshing signals, and other interfering factors often obfuscate fault signals, rendering the early detection of defects an arduous undertaking. Against this backdrop, this study presents an advanced approach for early defect detection, integrating the virtues of variational mode decomposition (VMD) and multiscale singular value decomposition (MSVD). Initially, a novel evaluation index is constructed by combining envelope entropy and envelope spectrum sparsity. Based on this a method is proposed to adaptively determine the critical parameters of VMD, enabling the adaptive decomposition of vibration signals into a series of modal components. The optimal sensitive components are then discerned utilizing the characteristic frequency intensity coefficient index. Subsequently, to address the limitations of single VMD methods in effectively suppressing low-frequency noise, the MSVD method is proposed for effective noise reduction, which reconstructs the signal after SVD of the signal within each segment through the operation of successive signal segmentation. Ultimately, envelope spectrum analysis is conducted on the reconstructed signal, facilitating the precise extraction of fault characteristic frequency information and enabling early fault identification. The efficacy of this novel methodology is evaluated through simulations and actual vibration signals, successfully discerning early faults afflicting rotating machinery.

A novel procedure to automate the removal of PLI and motion artifacts using mode decomposition to enhance pattern recognition of sEMG signals for myoelectric control of prosthesis

Hand Movement Recognition (HMR) with sEMG is crucial for artificial hand prostheses. HMR performance mostly depends on the feature information that is fed to the classifiers. However, sEMG often captures noise like power line interference (PLI) and motion artifacts. This may extract redundant and insignificant feature information, which can degrade HMR performance and increase computational complexity. This study aims to address these issues by proposing a novel procedure for automatically removing PLI and motion artifacts from experimental sEMG signals. This will make it possible to extract better features from the signal and improve the categorization of various hand movements. Empirical mode decomposition and energy entropy thresholding are utilized to select relevant mode components for artifact removal. Time domain features are then used to train classifiers (kNN, LDA, SVM) for hand movement categorization, achieving average accuracies of 92.36%, 93.63%, and 98.12%, respectively, across subjects. Additionally, muscle contraction efforts are classified into low, medium, and high categories using this technique. Validation is performed on data from ten subjects performing eight hand movement classes and three muscle contraction efforts with three surface electrode channels. Results indicate that the proposed preprocessing improves average accuracy by 9.55% with the SVM classifier, significantly reducing computational time.