Search Results (20,795)

Predictive maintenance aims to monitor equipment conditions through data-driven analysis and estimate failure in advance, which enables the scheduling of maintenance prior to equipment breakdown. In this work, a deep-learning neural network is used to predict the condition of the belt-drive system. A combined Convolutional Neural Network with Bi-directional Long Short-Term Memory (CNN-BiLSTM) model is assigned for processing the operational parameters along with vibrational signals to predict belt-drive system conditions in two separate binary classifications: faulty or healthy and unbalanced or balanced conditions. The data flow in the CNN-BiLSTM model initiates with the CNN to extract the features from the vibration signals and performs essential pattern detection. Consequently, the BiLSTM’s role is to capture long-term temporal relationships that cannot be captured by the CNN alone. To predict the targeted conditions, a fully connected layer with a classifier is built at the BiLSTM outputs. For efficient model training, the data is preprocessed through denoising, augmentation, and normalization. Additionally, hyperparameter tuning is conducted to explore different model configurations and select the optimal ones on the basis of relevant performance. An ablation study is conducted to investigate the use of CNN and BiLSTM models individually, confirming that combining both components is essential for accurate classification. Moreover, the cross-validation technique is used to assess the proposed model’s generality by organizing unseen data across rotational speeds, which also depicts robust performance under varying operating conditions. The key added value of this study lies in integrating deep-learning techniques to address a knowledge gap by using raw vibrational signals to establish intelligent monitoring systems, which represents a new scientific contribution to the predictive maintenance of belt-drive systems. Full article

Today, distributed optical fiber sensors are widely used in structural health monitoring due to their high sensitivity and long-distance applicability. However, when embedded in pavement structures, distributed optical fiber sensors are always installed in a slotted buried fashion, which not only affects current pavement durability but also reduces pavement construction efficiency. In order to design clear requirements of in situ-embedded distributed optical fiber sensors for pavement construction, this study analyzes the micro-mechanical behavior of optical cables under the ultimate pavement compaction state based on a coupled DEM-FDM approach. According to the study results, it is found that when the pavement subbase was compacted, the maximum contact force of 13.2 mm aggregates in the Z-direction exceeds 150 N, which is the main resistance of the external load during pavement construction. The tight-buffered optical cable without reinforcement element and armored layer cannot withstand the vibration load. The inclusion of GFRP strengthening components and an armored layer decreased maximum stress by 38.2% (X), 30.6% (Y), and 30.9% (Z), as well as displacement by 64.6% (X), 45.5% (Y), and 66.7% (Z). Additionally, the thickness of the outer sheath enhanced the ability to withstand tension but not compression. The increase in the thickness of the armored layer can improve the ability to withstand tension and compression. Full article

There is no consensus among historians when it comes to the importance of Vincenzo Galilei’s role in the history of music and science, especially when it comes to his contribution to the birth of modern experimentalism. Galilei’s written works, even those left in manuscript form, most of which have now been transcribed and published, do not provide a clear picture of his contribution. Moreover, there is a lack of private documents, such as letters, which informally describe his approach, working hypotheses, and doubts. Nevertheless, his writings enable us to conclude two things with certainty: he believed that reason-mediated experimentation was the only reliable source of knowledge, and he engaged in an intense and interesting experimental activity. Full article

In this work, we present a novel online acoustic sulfur hexafluoride (SF₆) monitoring system utilizing a miniaturized lithium niobate tuning fork (LNTF) sensor. The proposed system demonstrates enhanced stability and a broadband vibration–frequency response. The LNTF exhibits a fundamental resonance frequency of 32,901 Hz, and its quality factor (Q-factor) decreases from 19,700 to 18,300 as the SF₆ concentration increases at atmospheric pressure. Verification experiments at room temperature reveal a quantifiable correlation between the SF₆/N₂ mixture concentration ratio and the sensor’s mechanical impedance. Specifically, an impedance shift of 100 Ω corresponds to a concentration change of 0.0145 g/L. In air, with a signal integration time of 80 s, the measured noise voltage and current are 0.13 µV and 0.18 pA, respectively. These results underscore the potential of the LNTF as a compact, high-stability sensing platform for greenhouse gas monitoring in electrical infrastructure and industrial environments. Full article

Ice accretion on aircraft, wind-turbine blades, power networks, civil infrastructure, and exposed sensors poses severe safety risks and economic costs. Passive icephobic surfaces mitigate icing by delaying heterogeneous nucleation, altering droplet impact/solidification and wetting transitions, and/or weakening the ice–substrate bond so that accreted ice sheds under modest aerodynamic, gravitational, or vibrational loads. This review synthesizes recent progress using a unified mechanism framework linking (i) nucleation and early freezing, (ii) droplet dynamics during impact or condensation/frosting, and (iii) ice accretion and removal governed by interfacial fracture. Smooth low-surface-energy coatings, textured (superhydrophobic) surfaces, slippery liquid-infused porous surfaces (SLIPS), and low-interfacial-toughness strategies are critically compared in terms of achievable performance ranges, failure modes, durability limits, fabrication scalability, and test-method dependence. Ice-adhesion measurement approaches (push-off, pull-off/tensile, centrifugal) are assessed and a minimum reporting checklist is provided to improve comparability. Case studies across aviation, wind energy, power infrastructure, sensors, and emerging civil-engineering coatings highlight that durability and scale-dependent failure modes remain the dominant barriers to durable, energy-free icing mitigation. The review concludes with priorities for eco-friendly chemistries, self-healing or renewable layers, standardized testing/reporting, and data-driven (machine learning-assisted) optimization to accelerate translation into durable passive ice-mitigation technologies. Full article

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) and depression frequently occur together. Identifying the genes that influence both MASLD and depression may facilitate the discovery of biological pathways associated with disease risk. Methods: We recruited 525 participants from Mexican American families living in the Rio Grande Valley of south Texas. We collected clinical data, biometric measurements, hepatic health assessments using Vibration-Controlled Transient Elastography (VCTE), and depression evaluations determined with the Beck Depression Inventory-II. We estimated the heritability (h²) of MASLD-related measures, depression status, aspartate aminotransferase (AST), alanine aminotransferase (ALT), the AST/ALT ratio, and Vibration-Controlled Transient Elastography measurements. For each gene, we derived a genetic endophenotype representing its expression level. We then performed functional network and gene ontology enrichment analyses to characterize the underlying protein pathways. Results: We observed significant associations between the expression of two genes, Thyroid Hormone Receptor-Associated Protein 3 (THRAP3) (h² = 0.56 [0.45, 0.67]) and ADAM Metallopeptidase with Thrombospondin Type 1 Motif 7 (ADAMTS7) (h² = 0.66 [0.55, 0.77]), with depression and multiple MASLD-related phenotypes. We identified 351 genes with expression levels significantly correlated with one or more MASLD phenotypes and depression. Among these, five genes—ADAMTS7, THRAP3, CHPM4A, RAB9A, and PDIA3—were jointly associated with three phenotypes: AST/ALT, ALT, and Controlled Attenuation Parameter (CAP kPa). Based on the Fisher Combined Test, only THRAP3 (p = 3.0 × 10⁻²) and ADAMTS7 (p = 2 × 10⁻²) were jointly significant for depression (BDI-II) and AST, ALT, AST/ALT ratio, FAST, and CAP (kPa). We present a protein–protein interaction network comprising nodes (proteins) and edges (interactions), and a gene ontology enrichment analysis of cellular components. Discussion: Our findings highlight pleiotropic genes underlying MASLD and depression. Two genes, ADAMTS7 and THRAP3, warrant further investigation as potential targets for therapeutic interventions to manage MASLD and depression among Mexican Americans. These results may improve our understanding of the pathways involved in these two diseases, advance current research, and contribute to improvements in personalized medicine. Conclusion: We identified possible shared gene expression phenotypes linking MASLD and depression, which may provide insight into a common molecular underpinning. Pathway enrichment and gene analysis were used to help refine networks and enhance our understanding of complex gene-environmental interactions and their implications for precision medicine. Full article

Energy harvesting plays a pivotal role in enabling sustainable power supply for the Internet of Things and distributed sensor networks, particularly for low-power devices. Piezoelectric energy harvesters based on vortex-induced vibrations offer a promising solution for low-wind-speed applications, yet their performance is constrained by limited bandwidth and sensitivity to wind speed variations. This study addresses these limitations by proposing a novel multi-parameter adjustable piezoelectric energy harvester featuring an inclined cylindrical bluff body. By systematically tuning the inclination angle and installation position, the device achieves substantial performance improvements. Experimental results indicate that the optimized configuration yields a wider operational frequency band and enhanced energy conversion efficiency. Through the experimental results, we discovered the existence of the double-peak phenomenon and the plateau phenomenon. The voltage value of the second peak can reach up to 122.4% of the maximum voltage of the first peak. The duration of the maximum plateau phase can maintain between the wind speed of 2.3 m/s and 5.7 m/s. Full article

Potholes are typical negative road obstacles that can significantly compromise vehicle safety and ride comfort when traversed at inappropriate speeds. To address this issue, this paper proposes a pothole-detection-based, comfort-oriented pothole traversal algorithm that integrates multi-sensor fusion perception, comfort-constrained speed planning, and fuzzy control. A camera and a single-point ranging LiDAR are first fused to extract key geometric features of potholes, including contour, area, and depth. Based on these features, a vehicle–pothole dynamic model is developed in ADAMS to quantify the influence of pothole area and depth on vehicle vertical vibration. The vertical frequency-weighted root-mean-square (RMS) acceleration is adopted as the ride comfort indicator, based on which the maximum allowable traversal speed under different pothole geometries is determined. Furthermore, a longitudinal pothole traversal control strategy based on fuzzy theory is designed to regulate vehicle acceleration, enabling the vehicle to reach the comfort-constrained limiting speed within a finite preview distance while ensuring braking safety. The proposed method is validated through multi-scenario co-simulations using MATLAB/Simulink and CarSim, as well as real-vehicle experiments. Results demonstrate that the proposed strategy can effectively adjust vehicle speed before pothole traversal, satisfying comfort constraints and improving ride comfort without sacrificing driving safety. Full article

This paper presents an integrated numerical and experimental methodology for process-based condition monitoring, early-stage fault detection, and diagnosis to improve the operational safety and structural integrity of remotely operated working machines. Because operators have limited perception of hazardous conditions (e.g., resonance, high vibration, and transient dynamic loads), emerging faults may remain unnoticed. The framework identifies and tracks key diagnostic parameters—especially dynamic load indicators—enabling early detection of abnormal events that can initiate damage in the load-carrying structure and other critical components. A key challenge in designing and deploying such machines is limited knowledge of the occurrence, characteristics, and frequency of dynamic loads in real operations. Underestimating these loads during design can cause unexpected failures and reduced fatigue life. The approach integrates numerical strength simulations with sensor data collected during operation, correlating process signals with complex loading scenarios and hazard states. By combining model-based assessment with experimental validation, the method supports systematic process supervision and fault diagnosis under variable operating conditions. The methodology is demonstrated on an ARE 3.0 remotely operated machine case study and shows how data-informed loading characterization and early anomaly detection can enhance safety and support fatigue-oriented durability assessment. Full article

The evaluation of vibro-acoustic comfort in vehicle cabins is frequently limited by fragmented treatment of noise and vibration indicators and by the absence of structured, reproducible assessment frameworks. This study proposes an advanced, correlation-driven and process-oriented methodology for vibro-acoustic comfort evaluation, designed to support systematic analysis and decision-making across varying vehicle operating conditions. The proposed framework is formulated as a sequential process comprising experimental data acquisition, signal preprocessing, statistical correlation analysis, and decision-oriented interpretation. The framework was experimentally validated on five special-purpose armored platforms under both stationary and dynamic operating regimes, with repeated measurement trials to ensure robustness. Interior and exterior sound pressure levels, together with vibration-related parameters, are experimentally measured under stationary and dynamic operating regimes. Pearson correlation coefficients are employed to quantify interdependencies among vibro-acoustic variables and identify dominant contributors affecting comfort-related conditions. The results indicate statistically significant correlations between interior noise levels and selected vibration indicators, revealing distinct correlation patterns associated with different operating states. Based on these findings, correlation strength was classified as weak (|r| < 0.3), moderate (0.3 ≤ |r| < 0.6), and strong (|r| ≥ 0.6), enabling structured contributor ranking. The primary contribution of this work consists in elevating correlation analysis from a descriptive statistical technique to a formalized assessment process suitable for integration into predictive modeling and optimization workflows. The framework provides a transferable methodological structure, validated within the investigated vehicle category. Full article

To systematically investigate the influence of metro train types on the operational noise of elevated rail transit, this study conducted field measurements on elevated sections of the Wuhan Metro Yangluo Line, Wuhan Metro Line 2, and Guangzhou Metro Line 4, comparing the noise characteristics of 4-car A-type, 6-car B-type, and 4-car L-type trains operating at 70 ± 2 km/h. Analysis of sound pressure levels and frequency spectra at multiple points revealed that wheel-rail noise peaks occurred at 630 Hz and 2500 Hz for A-type trains, around 800 Hz for B-type trains, and within 800–1250 Hz for L-type trains, while bridge structure-borne noise was consistently concentrated in the 63–100 Hz low-frequency range. Distinct emission patterns were observed: at on-girder points, noise levels were highest for A-type trains, followed by B-type and then L-type trains, a trend potentially linked to axle loads; conversely, at under-girder points, the order reversed with L-type trains producing the highest noise. At points 7.5 m and 25 m from the track centerline, A-type and B-type trains exhibited similar noise levels, whereas L-type trains were slightly quieter. Furthermore, all three train types showed a consistent noise attenuation rate of approximately 6 dB(A) per doubling of distance from the track centerline. The findings will serve as a reference and basis for rail transit noise prediction and control. Full article

In fundamental physics, sensors operating below liquid helium temperatures are highly vulnerable to vibrations, which can affect the sensitivity, for example, of high-performance particle detectors. Pulse-tube refrigerators, while generating vibrations lower than those of conventional systems, may still introduce several disturbances. Hence, flexible thermal connections are a commonly used mechanical solution to mitigate these undesirable effects. Among the materials that can be used, ultra-high-purity aluminum (UHP-Al) has attracted the attention for low-amplitude-vibration cryogenic applications, including gravitational wave interferometry, quantum information systems, precision space instrumentation, and cryogenic resonators. Thus, the aim of the paper is the characterization of the mechanical and microstructure properties of three UHP-Als (i.e., 5N—99.999 wt%, 5N5—99.9995 wt% and 6N—99.9999 wt%) intended for the production of thermal flexible connections with low stiffness, specifically designed to reduce vibration transmission in cryogenic environments. Mechanical properties were evaluated through standard tensile tests from room (+25 °C) to low temperature (i.e., −150 °C), providing insights into yield strength, ultimate tensile strength, elongation and elastic modulus. In addition, the dynamic elastic modulus of material loads, at cryogenic conditions (i.e., about −180 °C), was determined by measuring the natural resonance frequency, thereby assessing the material’s response to vibrational. Moreover, an extensive microstructural analysis was conducted using electron backscatter diffraction and x-ray diffraction. The correlation between the observed microstructure and the elastic properties was systematically examined. The results underscore the pivotal role of microstructural characteristics in dictating the elastic behavior of UHP Als. Eventually, the analysis provides valuable guidelines for the materials employment inside cryogenic systems, where severe vibration control is critical to maintain high operational performance. Full article

This study presents the dynamic characterization of a gas turbine blade manufactured from two different nickel-based superalloys: on the first hand, a superalloy called René 80 and, on the second hand, a directionally solidified (DS) nickel-based anisotropic superalloy, investigated during the validation phase of the development process. Starting from the original CAD geometry, precise and very detailed finite-element models were developed, progressively refined and modified, and consequently validated to ensure mesh-independent modal predictions. The study examines multiple possible sources of discrepancy between experimentally measured and numerically predicted natural frequencies, including geometric deviations, grouping of different interesting points, broach-block test configuration, material anisotropy, and the influence of internal rib turbulators. Statistical analyses of dimensional variations revealed no significant correlation with the observed frequency scatter, redirecting the investigation toward material behavior and modeling fidelity. The inclusion of turbulators in the finite-element model proved essential, reducing prediction errors for the first two modes by approximately 2–3%. For the DS superalloy, the effect of grain orientation was evaluated over permissible angular deviations (extremes were considered); however, no systematic and clear improvement in frequency prediction was observed. Finally, several tuning strategies were assessed, leading to an optimization procedure that simultaneously adjusted the elastic moduli $E_x$ and $E_z$, reducing modal frequency deviations to below 1% for the first two modes. The proposed methodology provides a robust and solid framework for the validation of turbine blade dynamic behavior across different materials and manufacturing conditions. Full article

The identification/classification of textile fibres is essential in manufacturing, forensic science, cultural heritage preservation, and recycling. Conventional methods, including solubility tests, optical microscopy, and chromatographic techniques, are often destructive, labour-intensive, and limited in scope. Vibrational spectroscopy, particularly near-infrared (NIR), Fourier-transform infrared (FTIR), and Raman spectroscopy, has emerged as a rapid, non-destructive, and accurate alternative for fibre analysis. However, multi-composition textiles, dyes, finishing agents, and ageing effects frequently cause overlapping spectral features, hampering direct interpretation. This review examines the combined use of vibrational spectroscopy and chemometrics for textile fibre discrimination. It critically evaluates the performance of different spectroscopic techniques in classifying natural, synthetic, and blended fibres. The role of multivariate analysis methods, such as PCA, PLS, LDA, SIMCA, and machine learning algorithms, in improving spectral interpretation and classification accuracy is highlighted. Key factors affecting model robustness, including spectral pre-processing, sample heterogeneity, moisture, and colour, are also discussed. The integration of spectroscopy with chemometrics provides a robust, scalable, and sustainable solution for fibre identification, supporting quality control, fraud detection, and circular economy initiatives. This approach demonstrates significant potential for both research and industrial applications. Full article

Measurement results of train-induced vibrations are evaluated for characteristic frequencies, amplitudes and spectra, leading to a prediction which is based on transfer functions of the vehicle–track–soil system, the soil, and the building–soil system. The characteristic frequencies of train-induced vibrations are discussed following the propagation of vibrations from the source to the receiver: out-of-roundness frequencies of the wheels, the sleeper passage frequency, the vehicle–track eigenfrequency, the car-length frequency and multiples, axle-distance frequencies, bridge eigenfrequencies, the building–soil eigenfrequency, and floor eigenfrequencies. Amplitudes and spectra are compared for different train and track types, for different train speeds, and for different soft and stiff soils, where high frequencies are typically found for stiff soil and low frequencies for soft soil. The ground vibration is between the cut-on frequency due to the layering and the cut-off frequency due to the material damping of the soil, but the dominant frequency range also changes with distance from the track. The frequency band of the axle impulses due to the passing static loads obtains a signature from the axle sequence. The high amplitudes between the zeros of the axle-sequence spectrum are measured at the track, the bridge, and also in the ground vibrations, which are even dominant in the far field. A prediction software is presented, which includes all three parts: the excitation by the vehicle–track interaction, the wave transmission through the soil, and the transfer into a building. Full article