Search Results (205)

Gray-Box (GB) models are being considered for risk monitoring of Small Modular Reactors (SMRs). Their effectiveness is linked to the proper selection of the model parameters. This paper proposes a systematic methodology for identifying the most influential parameters of a GB model for estimating safety-critical variables of an SMR during normal operation and accident scenarios. The GB integrates a reduced-order physics-based model (White-Box, WB) with a data-driven (Black-Box, BB) model that corrects the outputs of the WB using the condition-monitoring data collected by sensors positioned onto the SMR. The proposed method combines signal decomposition, specifically the Hilbert–Huang Transform (HHT), and global sensitivity analysis (SA), based on first-order Kucherenko indices, to quantify the contribution of non-stationary, correlated GB input parameters to the variability of the safety-critical output parameters of interest. The proposed approach is applied to the Small Modular Dual Fluid Reactor (SMDFR), and the obtained results demonstrate its effectiveness in identifying informative and physically interpretable features, reducing complexity and computational burden to enable real-time risk monitoring. Full article

The operational reliability of gas-insulated switchgear/gas-insulated transmission lines (GIS/GIL) is critically threatened by internal metallic particles, which serve as primary triggers for insulation degradation. Conventional partial discharge (PD) detection methods often lack sensitivity during the early stages of particle movement. To overcome these limitations, this study aims to develop a novel non-intrusive defect diagnosis methodology based on the analysis of mechanical vibration signals. The coupled particle motion model integrating the electrostatic field, particle tracking, and multibody dynamics has been established. This model reveals the dynamic law that metallic particles migrate toward the conductor and undergo charge polarity reversal after collision, with a maximum speed of 2.7 m/s. Meanwhile, the peak vibration acceleration excited by the collision is calculated as 0.02 m/s². Accordingly, the high-voltage experimental platform with the full-scale prototype is built to simulate the actual operating conditions of the power grid. With the particle defects set inside the prototype, vibration signals are collected by using an accelerometer, and the measured peak vibration acceleration is 0.017 m/s². Finally, a defect diagnosis method based on the Hilbert–Huang Transform (HHT) and correlation coefficient analysis is proposed. This method uses Empirical Mode Decomposition (EMD) to extract the IMF4 component of the signal in the vicinity of the 1000 Hz frequency band. When particle defects occur, the correlation coefficient between the IMF4 component and the original signal exceeds 0.7668. This vibration-based monitoring technique provides an alternative for the condition-based maintenance of GIS/GIL, offering significant engineering value for enhancing the safety and reliability of power transmission infrastructure. Full article

In the deep and narrow valleys of southwestern China, free-surface spillways are widely adopted as auxiliary flood-discharge structures in water conservancy projects. Owing to the high water head upstream, tunnels are often plagued by problems including excessive velocity, cavitation damage, and insufficient downstream energy dissipation. Previous studies have demonstrated that the installation of novel lateral deflectors in tunnels can effectively regulate local flow patterns while providing additional energy dissipation capacity. In this study, physical model experiments combined with numerical simulations were employed to further compare the energy dissipation characteristics of lateral deflectors. The turbulent characteristics, the energy dissipation process, and the evolution of vortex structures were systematically analyzed based on turbulent kinetic energy, turbulence dissipation rate, fluctuating pressure coefficient, and Hilbert–Huang transform (HHT) spectral analysis. The results show that the novel lateral deflector significantly enhances local turbulence intensity and turbulent kinetic energy, promoting the conversion of mean kinetic energy into turbulent kinetic energy and its rapid dissipation within a shorter distance. Spectral energy reaches its peak in the jet impingement region, accompanied by a marked increase in high-frequency components, indicating an intensified energy transfer from large-scale vortices to small-scale vortices. These findings suggest that the novel deflector can serve as an effective internal energy dissipator in free-surface tunnels with shorter turbulent region and more local turbulence. Full article

To explore the spectral characteristics of near-field vibration signals from multi-hole millisecond-delay blasting in open-pit mines and the modulation effect of delay time on blasting energy distribution, field blasting vibration tests with multi-gradient delays were conducted taking an open-pit coal mine in Xinjiang as the engineering background. Particle Swarm Optimization (PSO) optimized Variational Mode Decomposition (VMD) and Hilbert-Huang Transform (HHT) were introduced for the refined processing and frequency band energy ratio analysis of the measured signals, and field vibration control tests were subsequently carried out. The results show that compared with the traditional Empirical Mode Decomposition (EMD), the PSO-optimized VMD can effectively overcome the mode aliasing phenomenon. By extracting the high-frequency Intrinsic Mode Function (IMF7) that characterizes the instantaneous detonation impulse, the actual delay time was successfully inverted to be 10.47 ms. The inter-hole delay time significantly affects the time-frequency distribution of vibration energy. Under the 25 ms delay condition, the energy ratio of the high-frequency band is the highest, and the low-frequency energy accumulation degree is the lowest, which is most conducive to shortening the vibration duration and accelerating energy attenuation. Control tests further confirmed that adopting a 17 ms delay in the near-slope area can effectively control the peak particle velocity (PPV) in the near field, while adopting a 23 ms delay in the middle and far areas can further reduce the low-frequency energy concentration. The research results demonstrate a dynamic matching strategy for millisecond delays based on spatial distance differences, which has important guiding significance for realizing safe and efficient blasting vibration control in open-pit mines. Full article

This study investigates the effectiveness of various feature extraction methods applied to vibration signals for the automatic detection of production defects in CNC (Computerised Numerical Control) milling machines. A dataset consisting of real-world data collected from CNC machines equipped with accelerometers was used. The objective of the study is to compare three main groups of techniques: time-domain analysis (TDA), frequency-domain analysis (FDA), and time–frequency-domain analysis (TFA). The findings indicate that basic TDA features lack the necessary sensitivity to accurately distinguish between Good Processing (GP) and Bad Processing (BP) states. Frequency-domain methods, such as the Fast Fourier Transform (FFT), median frequency calculation, and the Welch periodogram, provide better insights but still have limitations. The most effective results are obtained with TFA methods, particularly Empirical Mode Decomposition (EMD) and the Hilbert–Huang Transform (HHT), which reveal deeper signal characteristics. Following the feature optimisation studies, it was determined that a combination of four features—FMED, IMF2, IMF5 and WPT26—yielded the optimal performance, with an accuracy of 91.48%. The incorporation of a fifth feature resulted in information saturation within the model and did not improve performance. This study makes a novel contribution to literature by conducting an in-depth investigation into the most effective feature extraction and selection techniques for achieving robust discrimination between GP and BP productions using vibration signals in CNC milling processes. Conclusively, TFA features, supported by advanced signal processing, offer a strong basis for reliable, automated defect detection in CNC milling operations. Full article

Background/Objectives: Dementia is a progressive neurodegenerative disorder for which accurate and timely diagnosis remains a major clinical challenge. Electroencephalography (EEG) offers a noninvasive and cost-effective means of capturing neurophysiological alterations, motivating the development of reliable EEG-based automated diagnostic frameworks. This study aims to systematically examine how different time–frequency representations (TFRs) affect dementia classification performance within a unified multi-channel EEG image fusion framework. Methods: Resting-state, eyes-closed EEG recordings from 88 subjects, including Alzheimer’s disease, frontotemporal dementia, and cognitively normal controls, were preprocessed and segmented. Channel-wise signals were converted into two-dimensional time–frequency images using Short-Time Fourier Transform (STFT), Continuous Wavelet Transform (CWT), Hilbert–Huang Transform (HHT), Wigner–Ville Distribution (WVD), or Constant-Q Transform (CQT). Images from 19 EEG channels were fused into a structured grid and classified using pretrained convolutional neural networks, including MobileNetV2, ResNet-50, and InceptionV3. Results: Results indicate that classification performance is highly dependent on the chosen TFR. The STFT-based representation combined with InceptionV3 achieved the highest accuracy, reaching 98.8% with random splitting and 84.3% with subject-wise splitting, outperforming previous studies. CQT also showed competitive performance, whereas HHT and WVD were less effective. Gradient-weighted class activation mapping provided interpretable visualization of physiologically relevant EEG channel contributions. Conclusions: The proposed framework demonstrates the importance of structured multi-channel fusion and systematic TFR evaluation for robust and interpretable EEG-based dementia classification and serves as a foundation for future cross-dataset validation. Full article

The large deformation of soft rock within tunnels not only induces cracking in the initial supports and distortion of steel arches but also compromises the structural integrity of the secondary lining. In this study, we first examined the cracking characteristics of the secondary lining on both sides of the Baoligang Tunnel situated in a strong tectonic zone. A total of 257 cracks were identified, with 118 located on the left side of the tunnel and 139 on the right side. The triaxial compression test revealed that the failure characteristics of carbonaceous slate are mainly caused by shear slip failure due to the presence of weak bedding planes. Subsequently, a tailored blasting charge structure was designed to demolish the reinforced concrete secondary lining. This design incorporated a dense arrangement of blasting holes and interval charging techniques applied to the arch shoulders and sidewalls of the blasting zone, effectively fracturing the secondary lining in the left tunnel of the Baoligang Tunnel. Finally, an analysis was conducted based on vibration signals recorded during the dismantling process from three representative sections. The recorded vibration velocities from Case 1 indicate that the explosive charge has a relatively minor impact on the lining of the right tunnel. The peak particle velocity (PPV) recorded from the damaged lining closest to the blast center on the left side is 31.48 cm/s, exceeding the allowable vibration standard. Thereafter, the Hilbert–Huang Transform (HHT) was employed to identify the dominant frequency of the recorded vibration signals, which was determined to be 64 Hz. In Case 2, the PPVs at all monitoring points are below the vibration control standard for traffic tunnels. In Case 3, the PPVs suggest that the vibration has a minimal effect on the newly installed initial support. Full article

(1) Background: Electroencephalography (EEG) is widely used to monitor the depth of anesthesia; however, conventional Fourier-based analyses are limited in their ability to characterize non-stationary anesthetic-induced EEG dynamics. In this study, we investigated the utility of singular spectrum analysis (SSA) combined with the Hilbert transform for extracting physiologically meaningful EEG features under sevoflurane general anesthesia. (2) Methods: Frontal EEG data from ten patients undergoing sevoflurane anesthesia were analyzed from the maintenance phase through emergence. Using SSA, short EEG segments were decomposed into six intrinsic mode functions (IMFs) without pre-specified basis functions or frequency bands. Hilbert spectral analysis was applied to each IMF to obtain instantaneous frequency and amplitude characteristics. (3) Results: The SSA-based decomposition clearly captured phase-dependent EEG changes, including α spindle activity during maintenance and increasing high-frequency components preceding emergence. Multiple linear regression models incorporating IMF center frequencies and total power demonstrated strong correlations with the bispectral index (BIS), achieving high predictive accuracy (R² = 0.88, MAE < 4). Compared with conventional spectral approaches, SSA provided superior temporal resolution and stable feature extraction for non-stationary EEG signals. (4) Conclusions: These findings indicate that SSA combined with Hilbert analysis is a robust framework for quantitative EEG analysis during general anesthesia and may enhance real-time, individualized assessments of anesthetic depth. Full article

Partial discharge pattern recognition is a crucial task for assessing the insulation condition of Gas-Insulated Switchgear (GIS). However, the on-site environment presents challenges such as strong electromagnetic interference, leading to acquired signals with a low signal-to-noise ratio (SNR). Furthermore, traditional pattern recognition methods based on statistical parameters suffer from redundant and inefficient features that compromise classification accuracy, while existing artificial-intelligence-based classification methods lack the ability to quantify the uncertainty in defect classification. To address these issues, this paper proposes a novel GIS partial discharge pattern recognition method based on time–frequency energy grayscale maps and an improved variational Bayesian autoencoder. Firstly, a denoising-based approximate message passing algorithm is employed to sample and denoise the discharge signals, which enhances the SNR while simultaneously reducing the number of sampling points. Subsequently, a two-dimensional time–instantaneous frequency energy grayscale map of the discharge signal is constructed based on the Hilbert–Huang Transform and energy grayscale mapping, effectively extracting key time–frequency features. Finally, an improved variational Bayesian autoencoder is utilized for the unsupervised learning of the image features, establishing a GIS defect classification method with an associated confidence level by integrating probabilistic features. Validation based on measured data demonstrates the effectiveness of the proposed method. Full article

Snoring, a common sleep-disordered breathing phenomenon, impairs sleep quality for both the sufferer and any bed partner. While mild snoring primarily disrupts sleep continuity, severe cases often indicate obstructive sleep apnea (OSA), a disorder affecting 9–17% of the global population, linked to significant comorbidities and socioeconomic burden (see Introduction for supporting data). Here, we propose a low-cost, real-time snoring detection and intervention system that integrates a multiple-spectrum deep learning framework with an Internet of Things (IoT)-enabled smart pillow. The modified Parallel Convolutional Spatiotemporal Network (PCSN) combines three parallel convolutional neural network (CNN) branches processing Constant-Q Transform (CQT), Synchrosqueezing Wavelet Transform (SWT), and Hilbert–Huang Transform (HHT) features with a Long Short-Term Memory (LSTM) network to capture spatial and temporal characteristics of sounds associated with snoring. The smart pillow prototype incorporates two Micro-Electro-Mechanical System (MEMS) microphones, an ESP8266 off-shelf board, a speaker, and two vibration motors for real-time audio acquisition, cloud-based processing via Arduino cloud, and closed-loop haptic/audio feedback that encourages positional changes without fully awakening the snorers. Experiments demonstrated that the modified PCSN model achieves 98.33% accuracy, 99.29% sensitivity, 98.34% specificity, 98.3% recall, and 98.32% F1-score, outperforming existing systems. Hardware costs are under USD 8 and a smartphone app provides authorized users with real-time visualization and secure data access. This solution offers a cost-effective and accurate approach for home-based OSA screening and intervention. Full article

This paper comprehensively reviews advanced signal processing methods for partial discharge (PD) analysis, covering traditional time-frequency techniques, wavelet transform, Hilbert–Huang transform, and artificial intelligence-based methods. This paper critically examines the principles, advantages, limitations, and applicable scenarios of each method. A key contribution of this review is the systematic comparison of these methods, highlighting their evolution and complementary roles in processing non-stationary and noisy PD signals. However, a significant gap in current research remains the lack of standardized, explainable, and embeddable AI solutions for real-time, fine-grained PD classification. Future trends point to hybrid approaches and edge AI systems that combine physical insights with lightweight deep learning models to improve diagnostic accuracy and deployability. Full article

The extraction of geothermal energy is of great significance for sustainable energy development. The destruction of hard rock masses during geothermal well exploitation generates seismic waves that can compromise wellbore stability and operational sustainability. Seismic waves are known to be affected by rock structures like cracks and interfaces. However, a quantitative understanding of these effects on wave parameters is still lacking. This study addresses this gap by experimentally investigating the effect of crack geometry (angle and width) and rock interfaces on seismic wave propagation. Using a synchronous system for rock loading and seismic wave acquisition, we analyzed wave propagation through carbonate rock samples with pre-defined cracks and interfaces under unconfined, dry laboratory conditions. Key wave parameters (amplitude, frequency, and energy) were extracted using the fast Fourier transform (FFT) and the Hilbert–Huang transform (HHT). Our primary findings show the following: (1) Increasing the crack angle from 35° to 75° and the width from 1 mm to 3 mm leads to significant attenuation, reducing peak amplitude by up to 94.0% and energy by over 99.8%. (2) A tightly pressed rock interface also causes severe attenuation (94.2% in amplitude and 99.9% in energy) but can increase the main frequency by up to 8.5%, a phenomenon attributed to a “boundary effect”. (3) Seismic wave parameters exhibit significant spatial variations depending on the propagation path relative to the source and rock structures. This study provides a fundamental, quantitative baseline for how rock structures govern seismic wave attenuation and parameter shifts, which is crucial to improving microseismic monitoring and wellbore integrity assessment in geothermal engineering. Full article

Wind turbines face significant operational challenges due to their complex electromechanical systems, exposure to harsh environmental conditions, and high maintenance costs. Reliable structural health monitoring and condition monitoring are therefore essential for early fault detection, minimizing downtime, and optimizing maintenance strategies. Traditional approaches typically rely on either Trend Monitoring (TM) or Change Point Detection (CPD). TM methods track the long-term behaviour of process parameters, using statistical analysis or machine learning (ML) to identify abnormal patterns that may indicate emerging faults. In contrast, CPD techniques focus on detecting abrupt changes in time-series data, identifying shifts in mean, variance, or distribution, and providing accurate fault onset detection. While each approach has strengths, they also face limitations: TM effectively identifies fault type but lacks precision in timing, while CPD excels at locating fault occurrence but lacks detailed fault classification. This review critically examines the integration of TM and CPD methods for wind turbine diagnostics, highlighting their complementary strengths and weaknesses through an analysis of widely used TM techniques (e.g., Fast Fourier Transform, Wavelet Transform, Hilbert–Huang Transform, Empirical Mode Decomposition) and CPD methods (e.g., Bayesian Online Change Point Detection, Kullback–Leibler Divergence, Cumulative Sum). By combining both approaches, diagnostic accuracy can be enhanced, leveraging TM’s detailed fault characterization with CPD’s precise fault timing. The effectiveness of this synthesis is demonstrated in a case study on wind turbine blade fault diagnosis. Results shows that TM–CPD integration enhances early detection through coupling vibration and frequency trend analysis with robust statistical validation of fault onset. Full article

The Hard X-ray Modulation Telescope (HXMT), China’s first X-ray astronomy satellite, has significantly contributed to the study of fast variability in black hole X-ray binaries through its broad energy coverage (1–250 keV), high timing resolution, and sensitivity to hard X-rays. This review presents a comprehensive overview of timing analysis techniques applied to black hole X-ray binaries using Insight-HXMT data. We introduce the application and comparative strengths of several time-frequency analysis methods, including traditional Fourier analysis, wavelet transform, bicoherence analysis, and Hilbert-Huang transform. These methods offer complementary insights into the non-stationary and nonlinear variability patterns observed in black hole X-ray binaries, particularly during spectral state transitions and quasi-periodic oscillations. We discuss how each technique has been employed in recent Insight-HXMT studies to characterize timing features such as low-frequency QPOs, phase lags, and power spectrum evolution across different energy bands. Moreover, we present novel phenomena revealed by Insight-HXMT observations, including the detection of high-energy QPOs, spectral parameter modulation with QPO phase, and a new classification scheme for QPO types. The integration of multiple analysis methods enables a more nuanced understanding of the accretion dynamics and the geometry of the inner accretion flow, shedding light on fundamental physical processes in relativistic environments. Full article

Under the complex geological structural stress of the Western Himalayan syntaxis, the widespread distribution of hard and brittle rocks (such as sandstone and limestone) makes them prone to the formation of conjugate joints, also known as X-joints. These joints create weak structural planes in the slope rock mass, and when combined with weak interlayers within the slope, they result in a complex dynamic response and hazard situation in this region, which is further exacerbated by frequent seismic activity. This poses a serious threat to the planning, construction, and safe operation of the Belt and Road Initiative. To study the slope vibration response and instability mechanisms under these conditions, we conducted a shaking table test using the Iymek avalanche as a case study and performed Hilbert–Huang Transform (HHT) analysis. We also compared the results of the shaking table test on slope models without X-joints but containing weak interlayers. The findings show that the presence of X-joints leads to an earlier onset of plastic failure in the slope. During the failure development, X-joints cause stress concentration and the diversification of stress redistribution paths, delaying energy release. Ultimately, the avalanche failure mode in the X-joint slopes is more dispersed compared to the landslide failure mode in the model without X-joints. At the toe of the slope beneath the weak interlayer, low-frequency seismic waves can cause a significant amplification of acceleration, and the weak interlayer is often the shear outlets of the slope. These findings provide new insights into the seismic failure evolution of jointed slopes with weak interlayers and offer practical references for seismic hazard mitigation in mountainous infrastructure. Full article