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To reduce errors in sub-synchronous oscillation (SSO) modal identification and improve the accuracy and noise resistance of the traditional Prony algorithm, this paper focuses on SSOs caused by the integration of doubly fed induction generators (DFIGs) with series compensation into the grid. A novel SSO modal identification method based on the hippopotamus optimization–variational mode decomposition (HO-VMD) and singular value decomposition–regularized total least squares–Prony (SVD-RTLS-Prony) algorithms is proposed. First, the energy ratio function is used for real-time monitoring of the system to identify oscillation signals. Then, to address the limitations of the VMD algorithm, the HO algorithm's excellent optimization capabilities were utilized to improve the VMD algorithm, leading to preliminary denoising. Finally, the SVD-RTLS-improved Prony algorithm was employed to further suppress noise interference and extract oscillation characteristics, allowing for the accurate identification of SSO modes. The performance of the proposed method was evaluated using theoretical and practical models on the Matlab and PSCAD simulation platforms. The results indicate that the algorithms effectively perform denoising and accurately identify the characteristics of SSO signals, confirming its effectiveness, accuracy, superiority, and robustness against interference. [ABSTRACT FROM AUTHOR]

In this study, a lightweight convolutional neural network (CNN) is employed to identify Lamb modes. The proposed approach consists of five convolutional and pooling layers, then a fully-connected layer and a sigmoid layer. In which, the first convolutional layer is a wide-scale kernel. Lamb wave responses based on froward modelling are obtained for different plate materials (aluminium, steel and titanium), different excitation frequencies (250 kHz, 500 kHz), and different excitation cycles (4-cycle, 5-cycle). 16800 Lamb wave samples labelled by ‘A0 mode’ and ‘S0 mode’ are beforehand and hosted in a database, then trained via the lightweight CNN. In validation process, the lightweight CNN reaches 100% accuracy. The performance of light-weight CNN is also compared with some popular networks. Now, the well-trained network can be used to identify Lamb mode. Some responses are stimulated by ABAQUS under different excitation signal, different propagating distance, different plate material, and the predicted results via the lightweight CNN are all right. In addition, the extensibility of the network is validated by identifying new-converted Lamb mode correctly. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The collection and analysis of acoustic emission (AE) from weld circumferential cracks are crucial for ensuring pipeline safety. However, the unclear propagation characteristics and mode features of AE present challenges in array design and mode identification. In this manuscript, a novel mode feature extraction method for circumferential crack AE signals is proposed. This method involves three primary steps: firstly, a L-shaped flexible sensor array is designed to capture raw AE signals from both axial and circumferential directions. Next, the collected signals are filtered and evaluated to extract effective multi-mode components through the Kalman filtering and recursive plot (RP). Finally, the time–frequency features and mode types are extracted through combining the Hilbert-Huang transform (HHT) and dispersion curves. Results indicate that during the crack growths, both axial and circumferential direction AE signals contain multi-mode components, specifically L(0,1), F(1,1) and F(1,2) modes, with durations spanning 30–500 μs. Additionally, the axial modes predominantly occur within 200–300 kHz range, whereas the circumferential modes span both low and high frequency bands, specifically 40–50 kHz and 200–300 kHz, respectively. Modes across different frequencies indicate distinct structural behaviours, including crack growths and plastic deformations. The proposed method provides attempts for the pipe weld AE monitoring and multi-mode analysis. [ABSTRACT FROM AUTHOR]

The advent of data-driven/machine-learning based methods and the increase in data available from high-fidelity simulations and experiments has opened new pathways toward realizing reduced-order models for plasma systems that can aid in explaining the complex, multi-dimensional phenomena and enable forecasting and prediction of the systems' behavior. In this two-part article, we evaluate the utility and the generalizability of the dynamic mode decomposition (DMD) algorithm for data-driven analysis and reduced-order modeling of plasma dynamics in cross-field E × B configurations. The DMD algorithm is an interpretable data-driven method that finds a best-fit linear model describing the time evolution of spatiotemporally coherent structures (patterns) in data. We have applied the DMD to extensive high-fidelity datasets generated using a particle-in-cell (PIC) code based on the cost-efficient reduced-order PIC scheme. In this part, we first provide an overview of the concept of DMD and its underpinning proper orthogonal and singular value decomposition methods. Two of the main DMD variants are next introduced. We then present and discuss the results of the DMD application in terms of the identification and extraction of the dominant spatiotemporal modes from high-fidelity data over a range of simulation conditions. We demonstrate that the DMD variant based on variable projection optimization (OPT-DMD) outperforms the basic DMD method in identification of the modes underlying the data, leading to notably more reliable reconstruction of the ground-truth. Furthermore, we show in multiple test cases that the discrete frequency spectrum of OPT-DMD-extracted modes is consistent with the temporal spectrum from the fast Fourier transform of the data. This observation implies that the OPT-DMD augments the conventional spectral analyses by being able to uniquely reveal the spatial structure of the dominant modes in the frequency spectra, thus, yielding more accessible, comprehensive information on the spatiotemporal characteristics of the plasma phenomena. [ABSTRACT FROM AUTHOR]

Human–structure interaction is a critical element in the evaluation of human-induced structural vibration response. Compared to an empty structure, the crowds significantly modify the dynamics of a structure, such as the modes, natural frequencies, and the damping ratios of the structure. Several human dynamics models have been proposed to study human–structure interactions, and the most widely used one is single-degree-of-freedom (SDOF) mass–spring–damping (MSD) model due to its simplicity. The model equates the human body to a hybrid of three components, namely mass, spring, and damping. In this study, we only focus on the natural frequency and damping ratio. However, not enough research has been conducted in the field of civil engineering through actual structural test experiments to obtain the dynamic properties of human body. Therefore, more test results are required to provide a data basis for the future establishment of vibration serviceability assessments that are relevant to pedestrian–structure interaction. In this study, to obtain the results of parameter identification for an individual pedestrian, a full-size steel-concrete composite slab was excited at different frequencies and amplitudes. The chosen pace frequency of the pedestrian was 1.7 and 2.1 Hz. The results show that pedestrians are more sensitive to low-frequency vibrations and less sensitive to high-frequency vibrations. Furthermore, as vibration level of the structure increases, the natural frequency of the human body model decreases, while the damping ratio increases. [ABSTRACT FROM AUTHOR]

With advances in construction techniques and building materials, lightweight and flexible structures have been widely developed. Its light mass, low damping, and low frequency properties make it extremely vulnerable to human-induced vibration. Researchers have found that occupancy of a crowd would increase the damping of structures and decrease their natural frequencies. The adoption of a single-degree-of-freedom (SDOF) mass-spring-damper (MSD) model of the human body can illustrate the phenomenon. Therefore, to obtain the dynamic properties of the human body, we designed a full-scale experimental platform and measured the dynamic properties of the empty structure and the human-structure interaction system separately. The dynamic parameters of the human body in a standing position, a sitting position at different vibration levels were obtained. A total of 30 participants were involved in this study and the average frequency and damping ratio identified were 5.12 Hz and 36.76% respectively, when the body was in a standing position, and 4.92 Hz and 43.93% respectively when the body was in a sitting position. The results of the identification show that the human fundamental frequency decreases with increasing vibration magnitude and the human damping ratio tends to increase with increasing vibration magnitude, regardless of whether the human body is in a standing or sitting position. [ABSTRACT FROM AUTHOR]

Actual industrial processes often exhibit multimodal characteristics, and their data exhibit complex features, such as being dynamic, nonlinear, multimodal, and strongly coupled. Although many modeling approaches for process fault monitoring have been proposed in academia, due to the complexity of industrial data, challenges remain. Based on the concept of multimodal modeling, this paper proposes a multimodal process monitoring method based on the variable-length sliding window-mean augmented Dickey–Fuller (VLSW-MADF) test and dynamic locality-preserving principal component analysis (DLPPCA). In the offline stage, considering the fluctuation characteristics of data, the trend variables of data are extracted and input into VLSW-MADF for modal identification, and different modalities are modeled separately using DLPPCA. In the online monitoring phase, the previous moment's historical modal information is fully utilized, and modal identification is performed only when necessary to reduce computational cost. Finally, the proposed method is validated to be accurate and effective for modal identification, modeling, and online monitoring of multimodal processes in TE simulation and actual plant data. The proposed method improves the fault detection rate of multimodal process fault monitoring by about 14% compared to the classical DPCA method. [ABSTRACT FROM AUTHOR]

Gas path fault diagnosis is key to improving the reliability and safety of gas turbine engines. Flexible operating conditions bring obstacles to performing accurate gas path performance analysis. Most of the existing methods are developed for specific operating conditions, which are difficult to adapt to fully operating regions. The operating mode identification and targeted diagnostic model matching are effective technologies to solve the gas path fault diagnosis under fully operating regions, which improves diagnostic accuracy and efficiency. The fully operating regions are classified into four typical operating modes, and the targeted diagnostic models are matched according to the mode features. For the typical start-stop state and high dynamic state, the small deviation diagnostic model and transient diagnostic model are established and verified by real fault cases. The small deviation diagnostic model based on boundary parameters reduces the influences of operating conditions on diagnostic results, it accurately monitors the health states. The transient diagnostic model driven by the dynamic model and a designed hybrid solution algorithm markedly improves diagnostic accuracy and efficiency. It shows better performance for the mixed gas path fault modes, more stable diagnostic results, and higher diagnostic efficiency. The proposed technical framework provides an effective way for the fault diagnosis of gas turbine engines under fully operating regions. [ABSTRACT FROM AUTHOR]

In this work, we focus on the period-luminosity relation (PLR) of δ Sct stars, in which mode excitation and selection mechanisms are still poorly constrained, and whose structure and oscillations are affected by rotation. We review the PLRs in the recent literature, and add a new inference from a large sample of δ Sct. We highlight the difficulty in identifying the fundamental mode and show that rotation-induced surface effects can impact the measured luminosities, explaining the broadening of the PLR. We derive a tight relation between the low-order large separation and the fundamental radial mode frequency (F0) that holds for rotating stars, thus paving the way towards mode identification. We show that the PLRs we obtain for different samples are compatible with each other and with the recent literature, and with most observed δ Sct stars when taking rotation effects into account. We also find that the highest-amplitude peak in the frequency spectrum corresponds to the fundamental modein most δ Sct, thus shedding some light on their elusive mode selection mechanism. [ABSTRACT FROM AUTHOR]

The high yielding strength of advanced high-strength steel (AHSS) provides great opportunities for cold-formed steel (CFS) members with much higher load-carrying capability. However, if manufactured into the traditional cross-section shapes, such as C and Z, the material advantage cannot be fully exploited due to the cross-section instabilities. The purpose of this study was to establish a shape optimization method for cold-formed sections with AHSS and explore the potentially material efficiency that AHSS could provide to these sections in terms of their axial strength. In this study, the insights provided from the elastic buckling analysis and nonlinear finite element (FE) simulations of a set of traditional CFS sections were employed to determine the appropriate section size and length for optimization. Then, the optimization method was established using the particle swarm optimization (PSO) algorithm with the integration of computational analysis through CUFSM and the design approach (i.e., the direct strength method, DSM). The objective function is the maximum axial strength of the CFS sections manufactured with AHSS using the same amount of material (i.e., the same cross-section area). Finally, the optimal sections were simulated and verified by FE analysis, and the characteristics of the optimal cross-sections were analyzed. Overall, the optimization method in this paper achieved good optimization results with greatly improved axial strength capacity from the optimal sections. [ABSTRACT FROM AUTHOR]

Dynamic model-updating methods are a useful tool for obtaining high-precision finite element (FE) models. However, when using such methods to update a model, there will be problems with incompleteness and mode switching. To overcome these problems, this paper proposes a structural dynamic model-updating with an automatic mode-identification method. In this method, a mode-identification index is established based on image-similarity recognition to identify the consistency between FE and experimental mode shapes, and particle swarm optimization is introduced to update the model. In addition, to reduce the computational time, Latin hypercube sampling is employed to perform probability statistics of the switching range of the concerned mode orders, and the orders of mode identification are reduced according to the statistics results. In this paper, the proposed method was validated by model-updating of a square plate. The natural frequencies and mode shapes of the plate were obtained by experimental modal analysis and used as the updating objectives of the FE model. In addition, the boundary condition of the plate was simplified by a series of springs, which were used as updating parameters along with material properties and dimensions. Finally, the FE model of the plate was updated by the present method, and the results indicate that the objective function error of the updated FE model was successfully reduced from 14.31% to 1.05%, which proves that the proposed model-updating method is effective and feasible. [ABSTRACT FROM AUTHOR]

One unique feature of nonlinear dynamical systems is the existence of superharmonic and subharmonic resonances in addition to primary resonances. In this study, an effective vibration testing methodology is introduced for the experimental identification of these secondary resonances. The proposed method relies on phase-locked loop control combined with adaptive filters for online Fourier decomposition. To this end, the monotonic evolution of the phase lag around secondary resonances is exploited for their identification. The method is demonstrated using two systems featuring cubic nonlinearities, namely a numerical Duffing oscillator and a physical experiment comprising a clamped–clamped thin beam. The obtained results highlight that the control scheme can accurately characterize secondary resonances as well as track their backbone curves. A particularly salient feature of the developed algorithm is that, starting from the rest position, it facilitates an automatic and smooth dynamic state transfer toward one point of a subharmonic isolated branch, hence, inducing branch switching experimentally. • Develop a novel control-based method for secondary resonances identification. • Integrate PLL control with adaptive filters to enable real-time Fourier decomposition. • Effectively characterize folded superharmonic responses and subharmonic isolas. • Validate the proposed method using numerical and experimental demonstration. • Demonstrate an automatic dynamic state transfer process inducing branch switching. [ABSTRACT FROM AUTHOR]

With the widespread utilization of large-scale power electronics and the integration of wind power into power grid, the problem of low-frequency oscillations in new power systems become increasingly critical. In this paper, a mode identification-based model-free adaptive predictive control (MI-MFAPC) method with full-form dynamic linearization (FFDL) is proposed to design wide-area damping controller (WADC) to suppress multi-modal oscillations. Modal identification method is adopted in MFAPC-WADC to screen the controller input signals with high observability and low coupling, and the controller parameters are set separately for different oscillation modes. The improved control and adaptive law are proposed to rigorously guarantees the asymptotic convergence of the control error to zero under steady-state conditions. The proposed method integrates with the rolling optimization and introduces the future time input and output data, so it has strong robustness for the time-delay system. Simulation results show that MI-MFAPC-WADC greatly improves the dynamic quality and system stability, and the multiple oscillation modes are suppressed efficiently. Considering communication delays, the proposed method without the adaptive delay compensator even obtains better damping performance than the existed method with the adaptive delay compensator. • The FFDL-MFAPC method is designed to suppress multi-modal inter-area oscillation. • Modal identification method is adopted in MFAPC to screen the controller input signals.. • An improved control law is proposed for the supplementary WADC. • The design of the controller takes into account the communication delay. [ABSTRACT FROM AUTHOR]

Fan noise is a predominant noise component of the aero-engine, which consists of tonal noise and broadband noise. Duct mode calculation of random broadband noise is more complex than that of tonal noise. A probabilistic approach for duct acoustic mode identification is proposed to identify duct modes of broadband fan noise with high efficiency and accuracy. The inverse problem of mode identification is represented by a Bayesian framework based on a Gaussian-scale mixture prior model. Computational complexity and consuming time are remarkably decreased by approximating the Gaussian likelihood through a surrogate function. The block coordinate descent algorithm in the majorization–minimization framework is adopted for the indirect and iterative calculation of the mode coefficients. Simulations and experiments have verified the high computational efficiency afforded by the method, which provides more accurate results for duct mode identification of broadband noise, removes the influence of interfering modes on the recognition results, and allows better observation of the characteristics of the mode in a wide range of frequencies and rotating speeds. [ABSTRACT FROM AUTHOR]

The paper presents research on mode identification of broad-band oscillation in interconnected power system. A data-driven mode identification (DDMI) method for broad-band oscillation signals is proposed creatively in this paper. Firstly, piecewise aggregation approximation algorithm is improved to achieve effective dimension reduction of oscillation data. Combined with ${k}$ -Shape clustering algorithm, oscillation database is established with historical data, real-time data and simulation data. Then, oscillation mode identification models corresponding to different data categories can be obtained based on random forest algorithm, which can realize fast and automatic matching between broad-band oscillation data and oscillation mode parameters. Finally, the identification results of two simulation oscillation cases and an actual oscillation case show that proposed method can accurately identify the oscillation mode parameters from broad-band oscillation signals and has higher accuracy compared with other methods. [ABSTRACT FROM AUTHOR]

The modular multilevel converter (MMC) with the clamp double submodule (CDSM) performs outstandingly on the handling of DC fault current. As CDSM is composed of multiple switching devices, different operating modes can be produced according to the combination of switching states. Among the effective operating modes, there are inevitably some unexpected modes, which may cause abnormal operation or even failure of the MMC. In order to improve the reliability of MMC, it is necessary to identify all the effective operating modes of CDSM and analyze their trigger conditions. In this article, a novel mode identification method for power converter with multiple switches is used. According to reasonable combination of switching states, the components of CDSM are connected in an orderly manner to obtain all effective operating modes. Based on the operating principle of CDSM, the unexpected modes are further recognized from the effective operating modes, so that the impact of the unexpected modes on the MMC can be obtained. Finally, the recognition and analysis results of the unexpected modes in CDSM is verified by Simulink and Plecs RT Box.

The high yielding strength of advanced high-strength steel (AHSS) provides great opportunities for cold-formed steel (CFS) members with much higher load-carrying capability. However, if manufactured into the traditional cross-section shapes, such as C and Z, the material advantage cannot be fully exploited due to the cross-section instabilities. The purpose of this study was to establish a shape optimization method for cold-formed sections with AHSS and explore the potentially material efficiency that AHSS could provide to these sections in terms of their axial strength. In this study, the insights provided from the elastic buckling analysis and nonlinear finite element (FE) simulations of a set of traditional CFS sections were employed to determine the appropriate section size and length for optimization. Then, the optimization method was established using the particle swarm optimization (PSO) algorithm with the integration of computational analysis through CUFSM and the design approach (i.e., the direct strength method, DSM). The objective function is the maximum axial strength of the CFS sections manufactured with AHSS using the same amount of material (i.e., the same cross-section area). Finally, the optimal sections were simulated and verified by FE analysis, and the characteristics of the optimal cross-sections were analyzed. Overall, the optimization method in this paper achieved good optimization results with greatly improved axial strength capacity from the optimal sections.

A process plant can have multiple modes of operation due to varying demand, availability of resources, or the fundamental design of a process. Each of these modes is considered as normal operation. Anomalies in the process are characterized as deviations away from normal operation. Such anomalies can be indicative of developing faults which, if left unresolved, can lead to failures and unplanned downtime. The field Kalman filter (FKF) is a model-based approach, which is adopted in this article for monitoring a multimode process. Previously, the FKF has been applied in process monitoring to differentiate normal operation from known faulty modes of operation. This article extends the FKF so that it may detect occurrences of anomalies and differentiate them from the various normal modes of operation. A method is proposed for off-line training an FKF monitoring model and online monitoring. The off-line part comprises training an FKF model based on multivariate autoregressive state-space (MARSS) models fitted to historical process data. A monitoring indicator is also introduced. Online monitoring, based on the FKF for anomaly detection and mode identification, is demonstrated using a simulated multimode process. The performance of the proposed method is also demonstrated using data obtained from a pilot-scale multiphase flow facility. The results show that the method can be applied successfully for anomaly detection and mode identification. [ABSTRACT FROM AUTHOR]

Dynamic model-updating methods are a useful tool for obtaining high-precision finite element (FE) models. However, when using such methods to update a model, there will be problems with incompleteness and mode switching. To overcome these problems, this paper proposes a structural dynamic model-updating with an automatic mode-identification method. In this method, a mode-identification index is established based on image-similarity recognition to identify the consistency between FE and experimental mode shapes, and particle swarm optimization is introduced to update the model. In addition, to reduce the computational time, Latin hypercube sampling is employed to perform probability statistics of the switching range of the concerned mode orders, and the orders of mode identification are reduced according to the statistics results. In this paper, the proposed method was validated by model-updating of a square plate. The natural frequencies and mode shapes of the plate were obtained by experimental modal analysis and used as the updating objectives of the FE model. In addition, the boundary condition of the plate was simplified by a series of springs, which were used as updating parameters along with material properties and dimensions. Finally, the FE model of the plate was updated by the present method, and the results indicate that the objective function error of the updated FE model was successfully reduced from 14.31% to 1.05%, which proves that the proposed model-updating method is effective and feasible.

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents a new phasor measurement unit (PMU)-based wide-area damping control (WADC) method to suppress the critical inter-area modes of large-scale power systems. Modal participation factors, estimated by a practically model-free system identification approach, are used to select the most suitable synchronous generators for control through the proposed WADC algorithm. It is shown that multiple inter-area modes can be sufficiently damped by the proposed approach without affecting the rest of the modes, while only a few machines are needed to perform the control. The proposed technique is applied to the IEEE 68-bus and the IEEE 145-bus systems, including the test cases with PMU measurement noise and with missing PMUs. The simulation results clearly demonstrate the good adaptivity of the control strategy subjected to network model changes, its effective damping performance comparing to power system stabilizers (PSSs), and its great potential for near real-time implementation. [ABSTRACT FROM AUTHOR]

Multimode is the characteristic of industrial manufacturing processes due to different production strategies and environments. For multimode process monitoring, it is a challenge to identify different steady modes and transition modes. In this paper, a k nearest neighbours (KNN)‐based density peaks clustering (DPC) method is applied to identify different modes. First, the local density of each sample, which is obtained with a KNN constraint and its minimum distance to the higher local density points are calculated as two indicators of the DPC algorithm to find the cluster centres of the training data. Then, the transition modes are identified by combining the moving window strategy and the DPC algorithm, where an index called the local density‐distance ratio (LDDR) is employed. Finally, the monitoring algorithm is used to detect the faults for each operation mode. The effectiveness and advantages of the proposed method are illustrated by a numerical example and a Tennessee Eastman (TE) benchmark process. [ABSTRACT FROM AUTHOR]

The mode division and identification of the multimode process is to classify the normal production data according to the changes in process information and then to identify the corresponding process mode. The mode division and identification of the multimode process is the primary prerequisite for multimode process monitoring, evaluation, modeling, quality prediction, and process optimization. In the multimode complex industrial process, it usually contains both qualitative information and quantitative information, which makes the mode division and identification much more difficult. To deal with this problem, a new mode division and identification method is proposed in this article. First, the mode of the production process is roughly divided by the mode indicator variables that contain the initial production conditions with more qualitative variables. Then, the mode refinement is performed by using the process variables that contain a large amount of quantitative information. Therefore, the whole process is divided into multiple stable modes and transitional modes. At the end, the efficiency of the proposed method is illustrated with a case of the cyanide leaching process of gold hydrometallurgy. Note to Practitioners—From the practical application point of view, a new method of cross-referencing quantitative information model and qualitative information model is proposed in this article to divide the multimode production process into multiple stable modes and transitional modes. Then, feature models are established for different mode data online for process mode-type identification. The effective mode division and identification of multimode processes is the key to realize multimode process monitoring, evaluation, modeling, quality prediction, and process optimization. Although the validity of the proposed method is verified only in a gold hydrometallurgical cyanide leaching process, the method is equally applicable to other multimode complex industrial processes with the qualitative information and quantitative information coexistence characteristics. [ABSTRACT FROM AUTHOR]

The dominant modes of the system can be described by the critical eigenvalues, which reflect the stability and stability margin. This paper proposes a dominant mode identification (DMI) method to assess the stability of the grey-box grid-tied converter. First, the analytical relationship between the eigenvalues and the transfer function is clarified based on the small-signal model. Furthermore, the critical eigenvalues are estimated by the system's transfer function. On this basis, the discrete transfer function of the grey-box grid-tied converter is constructed utilizing the frequency sweeping technique. Finally, the critical eigenvalues are determined using the measured data based on the customized particle swarm optimization algorithm. The method can apply to different grid connection scenarios of converter-based components and is easy to implement. The DMI method can not only assess the stability margin of grey-box grid-tied converters in operation, but also predict the stability of black-box converters after being connected to the grid in the planning stage. • The relationship between the frequency responses and the eigenvalues is clarified. • The stability is analyzed according to the dominant mode of the system. • The method can pre-analyze the stability of the black-box converter. • The PSO algorithm for identifying dominant modes is easy to implement. [ABSTRACT FROM AUTHOR]

Scientific research is a continuous process, and the speed of future progress can be estimated by the pace of finding explanations for previous research questions. In this observer's based view of stellar pulsation and asteroseismology, we start with the earliest observations of variable stars and the techniques used to observe them. The earliest variable stars were large amplitude, radial pulsators but were followed by other classes of pulsating stars. As the field matured, we outline some cornerstones of research into pulsating star research with an emphasis on changes in observational techniques. Improvements from photographs, to photometry, CCDs, and space telescopes allowed researchers to separate out pulsating stars from other stars with light variations, recognize radial and non-radial pulsation courtesy of increased measurement precision, and then use non-radial pulsations to look inside the stars, which cannot be done any other way. We follow several highlighted problems to show that even with excellent space data, there still may not be quick theoretical explanations. As the result of technical changes, the structure of international organizations devoted to pulsating stars has changed, and an increasing number of conferences specialized to space missions or themes are held. Although there are still many unsolved problems, such as mode identification in non-asymptotic pulsating stars, the large amount of data with unprecedented precision provided by space missions (MOST, CoRoT, Kepler) and upcoming missions allow us to use asteroseismology to its full potential. However, the enormous flow of data will require new techniques to extract the science before the next missions. The future of asteroseismology will be successful if we learn from the past and improve with improved techniques, space missions, and a properly educated new generation.

Direct strength method (DSM) for the design of cold-formed steel members recommends finite strip method (FSM) for calculating the elastic buckling stresses corresponding to local, distortional and global buckling, which are considered to be the basic input parameters for design. This paper presents application of constrained spline finite strip method (cSFSM) developed by authors for calculating pure elastic buckling stresses in DSM and hence predicting the ultimate member capacity in uniform flexure and axial compression. The elastic buckling stresses are determined for a specified set of experiments available in literature using cSFSM by considering different end conditions and failure modes and the elastic stress values are applied in DSM for calculating ultimate member capacity. For beams with simply supported-warping free ends and columns with simply supported-warping fixed ends, the DSM evaluated results using cSFSM produces results comparable with experiments. The results obtained by the comparison of DSM with experiments were not satisfactory for fixed columns and beams with simply supported-warping fixed ends. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of designing a hybrid sliding mode observer for mode identification and actuator fault reconstruction, applicable for a class of linear switching systems. For each operating mode constituting the whole system, only one observer is doomed to accomplish the two aforementioned tasks in the presence of exogenous disturbances. The method uses H ∞ concept in the design of the sliding motion so that the effect of uncertainties on the residuals generation for mode recognition and on the reconstruction of the actuator faults will be minimized. Simulation results highlight the efficiency and the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The identification result of operational mode is eurychoric while operational mode identification is investigated under ambient excitation, which is influenced by the signal size and the time interval. The operational mode identification method, which is based on the sliding time window method and the eigensystem realization algorithm (ERA), is investigated to improve the identification accuracy and stability. Firstly, the theory of the ERA method is introduced. Secondly, the strategy for decomposition and implementation is put forward, including the buliding time window method and the filtration method of modes. At last. an example is studied, where the model of a cantilever beam is built and the white noise exciting is input. Results show that the operational mode identification method can realize the modes, and has high robustness to the signal to noise ratio and signal size. [ABSTRACT FROM AUTHOR]

• A mode identification method for slowly rotating n = 1 mode has been introduced using magnetic probes in KSTAR. • An ARX(autoregressive with exogenous terms) model has been applied to compensate the external non-axisymmetric field. • This identification method can adequately detect the mode-locking even in the presence of external magnetic perturbations. A mode identification method for slowly rotating (or non-rotating) n (toroidal mode number) = 1 plasma instabilities has been newly established with the signal compensation method used for an external time-varying magnetic field. The mode identification method is based on the Fourier decomposition scheme and processes the signal of the magnetic probes (MPs) on the passive stabilizer in the Korea Superconducting Tokamak Advanced Research (KSTAR) tokamak. To exclude the non-plasma magnetic perturbation by the external current coils and their secondary eddy currents on the passive stabilizer that may not be easily characterized, the ARX-SISO (autoregressive with exogenous terms-single input single output) method has been introduced in the signal compensation process. Preliminary off-line analysis presented herein confirms that this method can adequately detect the time evolution of the mode-locking, along with a slowly rotating state, even in the presence of external magnetic perturbations. [ABSTRACT FROM AUTHOR]

Thin-walled structural members have complex behavior. It is usual to interpret the complex behavior as the superposition of simpler behavior components, like global, distortional, local, in-plane shear and transverse extension. When the standard shell finite element method is employed for the analysis, the behavior components are not separated, and the results can be difficult to interpret due to the complexity of the deformations. The results are more meaningful if identified, i.e., if the participations of the behavior components are quantified. Methods for the formal identification of the deformations have already been proposed, by using either the modes of generalized beam theory, or the basis functions of the constrained finite strip method. In this paper a newer and more general method, the constrained finite element method is used. Since this method can handle a wide range of thin-walled members, including members with holes, or members with varying cross-sections, or even stiffened plates, the identification can readily be applied to various thin-walled members. In the paper some particularities of the identification method are highlighted, as well as several sample examples are presented and discussed. • The constrained finite element method is applied to modal identification. • Participation of global, distortional and local deformations are objectively calculated. • In the case of simple problems the results are similar to those from other identification methods. • The new method can handle a wide range of thin-walled members. • Examples are shown: channel members with holes, channel members with non-uniform cross-section, and stiffened plates. [ABSTRACT FROM AUTHOR]

Ultracapacitor (UC) based dc–dc power supplies are widely used for addressing surge power demands and to provide energy backup to critical loads. The conventional control techniques, such as unified control strategy, though ensure seamless mode transition, do not offer complete flexibility in charging and discharging controls which is crucial in UC-based backup systems. On the other hand, controls, such as independent switch control, though allow great flexibility in control, do not ensure seamless mode transition without appropriate mode-switch logic. This paper proposes a time-varying virtual resistance based mode transition control for independent switch control which not only ensures smooth, seamless transition between charging and discharging control modes but also ensures complete control over the mode transition durations. The proposed control is found to be robust to error mode identification as compared to introducing dead band between control modes. The proposed control is verified on a proof of concept experimental setup at a power level of $P_o=65$ W, voltage level of $V_g=26$ V with the dc–dc converter switching at $f_{\text{sw}}=100$ kHz. The performance comparison with PWM blocking method is also evaluated where the proposed control is found to work well. [ABSTRACT FROM AUTHOR]

This paper presents a new concept for calculating of the bond graph observer gains based on a graphical approach that investigates the Lyapunov Second Method in order to conclude about the observer stability and to search for the adequate gains that stabilize the observer. This new observer is used in the mode identification procedure. In fact, the observer's residues allow the differentiation between the current mode and the other system modes. In order to guarantee the efficiency of the mode identification procedure, a new bond graph approach is proposed. It concerns the discernibility between the modes in bond graph language. First, it deals with the R-discernibility that introduces the definition of the discernibility through the structural parity residual then returns to a simple calculation of some matrices' ranks in bond graph language. Second, another bond graph technique is applied to define the discernibility throughout the equivalency between the realizations. [ABSTRACT FROM AUTHOR]

Abstract Financial paper is a note without reason debt or consideration acceptance, issued for obtaining money financing. Financial paper identification system is a hot issue of the current file analysis and identification system, it covers paper classification, image processing, character segmentation and identification, file image compression and other series of processes. A research on multiple aspects of financial paper identification system is made in this paper. On which basis, a financial paper identification system with applied value is established. Through substantive experimental test and practical application, the method has better classification performance and higher processing efficiency, and has been used in bank bill identification processing system in a large scale. [ABSTRACT FROM AUTHOR]

Pretension deviation may cause stiffness degradation and overstress that can compromise the safety of tensile structures, which can be diagnosed by modal identification. This article presents modal tests on a 1:10 scaled model of a herringbone-ribbed cable dome structure. An optimal sensor placement scheme is proposed to observe the geometric stiffness change induced by pretension deviation. Based on the tests, different output-only modal identification techniques were implemented. A substructure strategy was adopted to overcome the limited measurement quantity and provide localized diagnoses. The experimental results show that operational modal analysis methods based on output-only data can effectively identify major modes of massive structures. The sensitivity of modal characteristics to pretension deviation is also evaluated via experimental comparisons, and modifications are implemented in an analytical finite element model to approximate the test model. The identified modal information can help locate stiffness degradation and thereby pretension loss in tensile structures. A modified modal strain energy method is proposed to detect pretension loss from decentralized testing and is verified by the test results. [ABSTRACT FROM AUTHOR]

Abstract In this paper, a representation learning based adaptive monitoring method for multimode processes is proposed, in which mode identification and fault detection are integrated with an adaptive threshold strategy. Compared to conventional methods, the learned representations can integrate mode features with fault details here, which are formed by the composition of multiple non-linear transformations under a global modeling. To explore the expressive powers of AE net, a geometry interpretation based on the reformulated sigmoid function is presented. Moreover, take the significance of the dynamic characteristics into account, an adaptive thresholding scheme is proposed for the learned representations based on a modified exponentially weighted moving average (EWMA) control chart. Experiment results show that the proposed method not only improves the divisibility between multimode, but also exhibits superior performance of fault detection on an industrial benchmark of chemical process, Tennessee Eastman process (TEP). Highlights • Mode identification and fault detection are integrated with an adaptive threshold strategy in the proposed monitoring method. • The learned representations that formed by the composition of multiple non-linear transformations can integrate mode features with fault details under a global modeling. • To explore the expressive powers of AE net, a geometry interpretation based on the reformulated sigmoid function is presented, and it is shown that AE is more efficient to approximate smooth functions. • Take the significance of dynamic characteristics into consideration, an adaptive thresholding scheme is proposed for the learned representations based on a modified exponentially weighted moving average control chart. • The contribution plots of statistics S R E and M 2 are developed for finding the causal variable or identifying the fault’s type. [ABSTRACT FROM AUTHOR]

Mode identification from post-disturbance “ringdown” responses can provide vital information concerning the dynamic performance and the stability margins of power systems. Therefore, several measurement-based identification techniques have been proposed in the literature to analyze ringdown responses of transmission systems and provide close to real-time estimation of the modal content. However, the applicability of these methods has not been thoroughly investigated for the analysis of active distribution networks (ADNs). Scope of this paper is to evaluate the applicability and the performance of eight measurement-based system identification techniques for the modal analysis of ADNs. The examined methods are used to identify the dominant oscillatory modes contained in ringdown responses of different types of signals. The Monte Carlo method is applied to investigate the influence of several parameters on the accuracy and efficiency of the identification procedure, while laboratory measurements are used to further demonstrate the accuracy of the examined methods. Practical issues encountered in the application of the identification techniques for the analysis of ADNs are discussed and potential solutions are proposed. Results reveal that although most of the examined techniques perform satisfactorily enough and thus can be readily employed for the modal analysis of ADNs, the Vector Fitting and the Hybrid FD/TD seem to be the most effective methods in terms of accuracy, robustness and computational efficiency. [ABSTRACT FROM AUTHOR]

Image-based vibration measurement has gained increased attentions in civil and construction communities. A recent video-based motion magnification method was developed to measure and visualize small structure motions. This new approach presents a potential for low-cost vibration measurement and mode shape identification. Pilot studies using this approach on simple rigid body structures were reported. Its validity on complex outdoor structures has not been investigated. In this study, a non-contact video-based approach for multi-point vibration measurement and mode magnification is introduced. The proposed approach can output a full-field vibration map that increases the efficiency of the current structural health monitoring (SHM) practice. The multi-point approach is developed based on the local phases which also fill the gap of the existing intensity-based multi-point vibration measurement. As an extension of the phase-based motion magnification, the multi-point measurement result is then integrated with the maximum likelihood estimation (MLE) to estimate the magnified frequency bands at each identified structure mode for operational deflection shape (ODS) visualization. This proposed method was tested in both indoor and outdoor environments for validation. The results show that using the developed method, mode frequencies and mode shapes of multiple points in complex structures can be simultaneously measured. And vibrations in each mode can be visualized separately after magnification. [ABSTRACT FROM AUTHOR]

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