Search Results (12)

Fault diagnosis is one of the most important issues for a modular multilevel converter (MMC). However, conventional solutions are deficient in two aspects. Firstly, they lack the necessary feature information. Secondly, they are incapable of performing open-circuit fault diagnosis of the modular multilevel converter with the requisite degree of accuracy. To solve this problem, an intelligent diagnosis method is proposed to integrate the modal time–frequency diagram and FFT-CNN-BiGRU-Attention. By selecting the phase current and bridge arm voltage as the core fault parameters, the particle swarm algorithm is used to optimize the Variational Modal Decomposition parameters, and the fault signal is decomposed and reconstructed into sensitive feature components. The reconstructed signals are further transformed into modal time–frequency diagrams via continuous wavelet transform to fully retain the time–frequency domain features. In the model construction stage, the frequency–domain features are first extracted using the fast Fourier transform (FFT), and the local patterns are captured through a combination with a convolutional neural network; subsequently, the timing correlations are analyzed using bidirectional gated loop cells, and the Attention Mechanism is introduced to strengthen the key features. Simulations show that the proposed method achieves 98.63% accuracy in locating faulty insulated gate bipolar transistors (IGBTs) in the sub-module, with second-level real-time response capability. Compared with the recently published scheme, it maintains stable performance under complex working conditions such as noise interference and data imbalances, showing stronger robustness and practical value. This study provides a new idea for the intelligent operation and maintenance of power electronic devices, which can be extended to the fault diagnosis of other power equipment in the future. Full article

In the domain of micromotion target recognition, target characteristics can be extracted through either broadband or narrowband radar echoes. However, due to technical limitations and cost constraints in acquiring broadband radar waveform data, recognition can often only be performed through narrowband radar waveforms. To fully utilize the information embedded within narrowband radar waveforms, it is necessary to conduct in-depth research on multi-dimensional features of micromotion targets, including radar cross-sections (RCSs), time frequency (TF) images, and cadence velocity diagrams (CVDs). To address the limitations of existing identification methodologies in achieving accurate recognition with narrowband echoes, this paper proposes a graph fusion network based on a cross-modal attention mechanism, termed GF-AM Net. The network first adopts convolutional neural networks (CNNs) to extract unimodal features from RCSs, TF images, and CVDs independently. Subsequently, a cross-modal attention mechanism integrates these extracted features into a graph structure, achieving multi-level interactions among unimodal, bimodal, and trimodal features. Finally, the fused features are input into a classification module to accomplish narrowband radar micromotion target identification. Experimental results demonstrate that the proposed methodology successfully captures potential correlations between modal features by incorporating cross-modal multi-level information interactions across different processing stages, exhibiting exceptional accuracy and robustness in narrowband radar micromotion target identification tasks. Full article

This paper presents a comprehensive review of automated modal identification techniques, focusing on various established and emerging methods, particularly Stochastic Subspace Identification (SSI). Automated modal identification plays a crucial role in structural health monitoring (SHM) by extracting key modal parameters such as natural frequencies, damping ratios, and mode shapes from vibration data. To address the limitations of traditional manual methods, several approaches have been developed to automate this process. Among these, SSI stands out as one of the most effective time-domain methods due to its robustness in handling noisy environments and closely spaced modes. This review examines SSI-based algorithms, covering essential components such as system identification, noise mode elimination, stabilization diagram interpretation, and clustering techniques for mode identification. Advanced SSI implementations that incorporate real-time recursive estimation, adaptive stabilization criteria, and automated mode selection are also discussed. Additionally, the review covers frequency-domain methods like Frequency Domain Decomposition (FDD) and Enhanced Frequency Domain Decomposition (EFDD), highlighting their application in spectral analysis and modal parameter extraction. Techniques based on machine learning (ML), deep learning (DL), and artificial intelligence (AI) are explored for their ability to automate feature extraction, classification, and decision making in large-scale SHM systems. This review concludes by highlighting the current challenges, such as computational demands and data management, and proposing future directions for research in automated modal analysis to support resilient, sustainable infrastructure. Full article

At present, researchers in the field of pipeline inspection focus on pipe wall defects while neglecting pipeline defects in special situations such as welds. This poses a threat to the safe operation of projects. In this paper, a multi-node fusion and modal projection algorithm of steel pipes based on guided wave technology is proposed. Through an ANSYS numerical simulation, research is conducted to achieve the identification, localization, and quantification of axial cracks on the surface of straight pipelines and internal cracks in circumferential welds. The propagation characteristics and vibration law of ultrasonic guided waves are theoretically solved by the semi-analytical finite element method in the pipeline. The model section is discretized in one-dimensional polar coordinates to obtain the dispersion curve of the steel pipe. The T(0,1) mode, which is modulated by the Hanning window, is selected to simulate the axial crack of the pipeline and the L(0,2) mode to simulate the crack in the weld, and the correctness of the dispersion curve is verified. The results show that the T(0,1) and L(0,2) modes are successfully excited, and they are sensitive to axial and circumferential cracks. The time–frequency diagram of wavelet transform and the time domain diagram of the crack signal of Hilbert transform are used to identify the echo signal. The first wave packet peak point and group velocity are used to locate the crack. The pure signal of the crack is extracted from the simulation data, and the variation law between the reflection coefficient and the circumferential and radial dimensions of the defect is calculated to evaluate the size of the defect. This provides a new and feasible method for steel pipe defect detection. Full article

Electric motors are indispensable electrical equipment in ships, with a wide range of applications. They can serve as auxiliary devices for propulsion, such as air compressors, anchor winches, and pumps, and are also used in propulsion systems; ensuring the safe and reliable operation of motors is crucial for ships. Existing deep learning methods typically target motors under a specific operating state and are susceptible to noise during feature extraction. To address these issues, this paper proposes a Resformer model based on bimodal input. First, vibration signals are transformed into time–frequency diagrams using continuous wavelet transform (CWT), and three-phase current signals are converted into Park vector modulus (PVM) signals through Park transformation. The time–frequency diagrams and PVM signals are then aligned in the time sequence to be used as bimodal input samples. The analysis of time–frequency images and PVM signals indicates that the same fault condition under different loads but at the same speed exhibits certain similarities. Therefore, data from the same fault condition under different loads but at the same speed are combined for cross-domain motor fault diagnosis. The proposed Resformer model combines the powerful spatial feature extraction capabilities of the Swin-t model with the excellent fine feature extraction and efficient training performance of the ResNet model. Experimental results show that the Resformer model can effectively diagnose cross-domain motor faults and maintains performance even under different noise conditions. Compared with single-modal models (VGG-11, ResNet, ResNeXt, and Swin-t), dual-modal models (MLP-Transformer and LSTM-Transformer), and other large models (Swin-s, Swin-b, and VGG-19), the Resformer model exhibits superior overall performance. This validates the method’s effectiveness and accuracy in the intelligent recognition of common cross-domain motor faults. Full article

The condition-based maintenance of vibrating screens requires new methods of their elements’ diagnostics due to severe disturbances in measured signals from vibrators and falling pieces of material. The bolted joints of the sieving deck, when failed, require a lot of time and workforce for repair. In this research, the authors proposed the model-based diagnostic method based on modal analysis of the 2-DOF system, which accounts for the interaction of the screen body and the upper deck under conditions of bolted joint degradation. It is shown that the second natural mode with an out-of-phase motion of the upper deck against the main screen housing may coincide with the excitation frequency or its higher harmonics, which appear when vibrators’ bearings are in bad condition. This interaction speeds up bolt loosening and joint opening by the dynamical loading of higher amplitude. The proposed approach can be used to detune the system from resonance and anti-resonance to reduce maintenance costs and energy consumption. To prevent abrupt failures, such parameters as second natural mode frequency, damping factor, and phase space plot (PSP) distortion measures are proposed as bolt health indicators, and these are verified on the laboratory vibrating screen. Also, the robustness is tested by the impulsive non-Gaussian noise addition to the measurement data. A special diagram was proposed for the bolted joints’ strength capacity assessment and maintenance actions planning (tightening, replacement), depending on clearance in the joints. Full article

Many parameters can be used to express a machine’s condition and to track its evolution through time, such as modal parameters extracted from vibration signals. Operational Modal Analysis (OMA), commonly used to extract modal parameters from systems under operating conditions, was successfully employed in many monitoring systems, but its application in rotating machinery is still in development due to the distinct characteristics of this system. To implement efficient monitoring systems based on OMA, it is essential to automatically extract the modal parameters, which several studies have proposed in the literature. However, these algorithms are usually developed to deal with structures that have different characteristics when compared to rotating machinery, and, therefore, work poorly or do not work with this kind of system. Thus, this paper proposes, and has as its main novelty in, a new automated algorithm to carry out modal parameter identification on rotating machinery through OMA. The proposed technique was applied in two different datasets to enable the evaluation of the robustness to different systems and test conditions. It is revealed that the proposed algorithm is suitable for the accurate extraction of frequencies and damping ratios from the stabilization diagram, for both the rotor and the foundation, and only one user defined parameter is required. Full article

The bifurcation process of self-sustained combustion instability pressure perturbations in a liquid rocket combustor is investigated based on the Helmholtz equations and a pressure dependent flame describing function. The modal frequency and growth rates are numerically resolved by the commercial software COMSOL multiphysics. Validation of the numerical approach is firstly conducted on a Rijke tube combustor, and a supercritical bifurcation for the first longitudinal mode is observed. The bifurcation diagrams for the first transverse mode for different time delays and gain index of the flame describing function are analyzed. Only the supercritical bifurcation presents for this configuration. The trajectory of Hopf points and the bifurcation diagram feature period motions with increasing the time delay. The effect of flame length distributions on the bifurcation diagrams is analyzed by considering a non-uniform flame length distribution model. Results show that the distribution has a large impact on the bifurcation process, e.g., the first transverse mode is more unstable for the non-uniform distribution. Finally, a subcritical bifurcation is found when a more complicated flame describing function is considered; the bistable region presents and the condition for this is discussed. Full article

Dynamic analysis of a large, full-scale construction requires proper excitation in order to induce vibrations that can be measured and further processed. The amount of delivered energy over the frequency band must be sufficient to excite all the mode shapes in the studied range. The paper concerns the pseudo-impulse pull-and-release method that allows to determine frequency response functions of a large, lightly damped structure and estimate its modal parameters. The main advantage of the developed method is the great independence of the repetitiveness of the experiment’s operational parameters. The output time histories from subsequent partial experiments are accurately synchronised and normalised without measuring the signal of input excitation. The research conducted for the full-scale transmission tower results in modal parameters, estimated by classical and pseudo-impulse methods. The applied pseudo-impulse improves the conditioning of the excitation and results in the better readability of stabilisation diagrams as well as in a better stabilisation of the poles that are not clearly represented in the input data. The proposed method allows for the visualisation of poles which are non-detectable in cases of classical analysis. Full article

This paper presents an extension of the formulation of wave propagation in transverse electric (TE) and transverse magnetic (TM) modes for the case of metallic cylindrical waveguides filled with longitudinally magnetized ferrite. The higher order modes were exploited. We externally controlled the cut-off frequency through the application of DC magnetic fields. The numerical results of dispersion diagrams for TE and TM modes were obtained and analyzed. We analyzed a waveguide antenna filled with partially magnetized ferrite using the mode matching (MM) technique based on the TE and TM modes. By using modal analysis, our approach considerably reduced the computation time compared to HFSS. Ferrites are important for various industrial applications, such as circulators, isolators, antennas and filters. Full article

Localized resonance phononic crystals (LRPCs) are increasingly attracting scientists’ attention in the field of low-frequency noise reduction because of the excellent subwavelength band gap characteristics in the low-frequency band. However, the LRPCs have always had the disadvantage that the noise reduction band is too narrow. In this paper, in order to solve this problem, LRPCs based on double-layer plates with cavity structures are designed. First, the energy bands of phononic crystals plate with different thicknesses were calculated by the finite element method (FEM). At the same time, the mechanism of band gap generation was analyzed in combination with the modalities. Additionally, the influence of structure on the sound transmission loss (STL) of the phononic crystals plate and the phononic crystals cavity plates were analyzed, which indicates that the phononic crystals cavity plates have notable characteristics and advantages. Moreover, this study reveals a unique ”cavity cave” pattern in the STL diagram for the phononic crystals cavity plates, and it was analyzed. Finally, the influence of structural factors on the band structure and STL of phononic crystals cavity plates are summarized, and the theoretical basis and method guidance for the study of phononic crystals cavity plates are provided. New ideas are also provided for the future design and research of phononic crystals plate along with potential applications in low-frequency noise reduction. Full article

This paper pursues a simultaneous modal parameter anomaly detection paradigm to structural damage identification inferred from vibration-based structural health monitoring (SHM) sensors, e.g., accelerometers. System Realization Using Information Matrix (SRIM) method is performed in short duration sweeping time windows for identification of state matrices, and then, modal parameters with enhanced automation. Stable modal poles collected from stability diagrams are clustered and fed into the Gaussian distribution-based anomaly detection platform. Different anomaly thresholds are examined both on frequency and damping ratio terms taking two testbed bridge structures as application means, and simplistic Boolean Operators are performed to merge univariate anomalies. The first bridge is a reinforced concrete bridge subjected to incremental damage through a series of seismic shake table experiments conducted at the University of Nevada, Reno. The second bridge is a steel arch structure at Columbia University Morningside Campus, which reflects no damage throughout the measurements, unlike the first one. Two large-scale implementations indicate the realistic performance of automated modal analysis and anomaly recognition with minimal human intervention in terms of parameter extraction and learning supervision. Anomaly detection performance, presented in this paper, shows variation according to the designated thresholds, and hence, the information retrieval metrics being considered. The methodology is well-fitted to SHM problems which require sole data-driven, scalable, and fully autonomous perspectives. Full article