Search Results (1,037)

Active suspensions in automotive applications are designed to improve vehicle stability and comfort and reduce vibration transmission from the road surface. Active systems often include a dedicated actuator, and, to reduce their mass and energy absorption, it is a typical choice to rely on brushless electric motors with permanent magnets containing Critical Raw Materials such as Neodymium, a Rare Earth Element (REE), offering favorable power density values. Although these systems offer clear advantages in terms of ride quality and performance, their direct and indirect energy requirements, combined with their dependence on resource-intensive materials, raise concerns about life cycle sustainability: in other words, there is a trade-off between production impact (relevant for REE) and use impact (reduced by REE adoption). To address this issue, the research proposes a method to estimate energy consumption during the use phase of a vehicle through a dedicated parametric modeling and simulation framework; the aim is to evaluate the energy performance of active suspension systems under different road and driving conditions. The analysis explores how design parameters and operational choices affect energy consumption and efficiency. The simulation results reveal a marked sensitivity of system performance to road profiles and driving scenarios, highlighting the importance of holistic assessments during the early stages of design. The proposed framework represents a first step toward integrating circular design principles into the development of active suspensions. By combining technical and environmental perspectives, it supports the development of next-generation automotive components that balance comfort, performance, and sustainability. Full article

Vibration and noise generated by rail transit systems pose significant constraints on their environmental sustainability. Although extensive research has been conducted by scholars on vibration and noise in rail transit, quantitative studies specifically investigating the influence of train speed on the vibration and noise of elevated rail transit are scarce. Therefore, this study selected a typical elevated section of Wuhan Metro Line 21 and systematically performed field tests to measure the vibration and noise induced by trains passing at speeds of 20, 40, 60 and 80 km·h⁻¹. Based on the test results, the vibration characteristics of the rails, track slab, and bridge structure, as well as the radiation characteristics of wheel–rail noise and bridge structure-borne noise under different speeds, were investigated. The study further explored the impact of train speed variation on the vibration and noise of the elevated rail transit system. The results indicate that the vibration acceleration levels of both the outer and inner rails increase significantly with train speed. Each time the speed doubles, the vibration level rises by approximately 11.5 dB for the outer rail and 10.0 dB for the inner rail. The vibration of the track slab and bridge structure is notably lower than that of the rails. Each time the speed doubles, the vibration acceleration level at various measurement points increases by an average of about 8.5–9.0 dB. Wheel–rail noise is primarily concentrated in the frequency bands around 630 Hz and 3150 Hz. Each time the speed doubles, the trackside noise level increases by an average of approximately 7.2–7.6 dB(A). Noise measured under the bridge shows a distinct peak around 100 Hz, which aligns with the vibration frequency of the bottom slab. Due to the shielding effect of shrubs, noise in the 63–100 Hz frequency band is attenuated at measurement points above ground level. Each time the speed doubles, bridge structure-borne noise increases by about 4.5–5.0 dB(A), representing a lower growth rate compared to wheel–rail noise. The findings of this research are expected to contribute to vibration and noise reduction strategies and support the sustainable development of rail transit systems. Full article

Against the global carbon neutrality target, predictive maintenance (PdM) of automotive engines represents a core technical strategy to advance the sustainable development of the automotive industry. Conventional single-modal diagnostic approaches for engine abnormal sound defects suffer from low accuracy and weak anti-interference capability. Existing multi-modal fusion methods fail to deeply mine the physical coupling between cross-modal features and often entail excessive model complexity, hindering deployment on resource-constrained on-board edge devices. To resolve these limitations, this study proposes a Physical Prior-Embedded Cross-Modal Attention (PPE-CMA) mechanism for lightweight multi-modal fusion diagnosis of engine abnormal sound defects. First, wavelet packet decomposition (WPD) and mel-frequency cepstral coefficients (MFCC) are integrated to extract time-frequency features from engine audio signals, while a channel-pruned ResNet18 is employed to extract spatial features from engine thermal imaging and vibration visualization images. Second, the PPE-CMA module is designed to adaptively assign attention weights to audio and image features by exploiting the physical coupling between engine fault acoustic and visual characteristics, enabling efficient cross-modal feature fusion with redundant information suppression. A rigorous theoretical derivation is provided to link cosine similarity with the physical correlation of engine fault acoustic-visual features, justifying the attention weight constraint (β = 1 − α) from the perspective of fault feature physical coupling. Third, an improved lightweight XGBoost classifier is constructed for fault classification, and a hybrid data augmentation strategy customized for engine multi-modal data is proposed to address the small-sample challenge in industrial applications. Ablation experiments on ResNet18 pruning ratios verify the optimal trade-off between diagnostic performance and computational efficiency, while feature distribution analysis validates the authenticity and effectiveness of the hybrid augmentation strategy. Experimental results on a self-constructed multi-modal dataset show that the proposed method achieves 98.7% diagnostic accuracy and a 98.2% F1-score, retaining 96.5% accuracy under 90 dB high-level environmental noise, with an end-to-end inference speed of 0.8 ms per sample (including preprocessing, feature extraction, and classification). Cross-engine and cross-domain validation on a 2.0T diesel engine small-sample dataset and the open-source SEMFault-2024 dataset yield average accuracies of 94.8% and 95.2%, respectively, demonstrating strong generalization. This method effectively enhances the accuracy and robustness of engine abnormal sound defect diagnosis, offering a lightweight technical solution for on-board real-time fault diagnosis and in-plant online quality inspection. By reducing engine fault-induced energy loss and spare parts waste, it further promotes energy conservation and emission reduction in the automotive industry. Quantified experimental data on fuel efficiency improvement and carbon emission reduction are provided to substantiate the ecological benefits of the proposed framework. Full article

This work presents an original implementation of the digital sampling pipeline for spaceborne Fourier Transform Spectrometers (FTSs). The implementation aims at improving the robustness of the spectrometer to harsh environmental conditions, including mechanical vibrations and a wide operational temperature range, avoiding the use of dedicated electronic hardware for the interferometer mirrors’ speed control and interferogram sampling. The FTS configuration is based on the constant time step sampling of the interferometer using a standard ADC (Analogue to Digital Converter), along with two metrology laser channels. The development tool is a MATLAB-based simulator developed to emulate the FTS and, in particular, the generation and acquisition of interferograms, incorporating harmonic vibrations and detector noise. The simulator was exploited to compare state-of-the-art techniques and newly implemented variants. An improvement of the arccosine method is first proposed, revising the normalisation process to exploit the full set of recorded data without discarding critical points. Subsequently, methods using two reference channels have been developed and evaluated. Two implementations are considered: two references at the same wavelength with an optimised phase shift (i.e., π/2) and two references at different wavelengths. Different data fusion strategies are compared in terms of spectral uncertainty, varying types of simulated disturbances and noise amplitudes. Results show that the optimal combination of two same-wavelength references consistently outperforms any other configuration, yielding lower average spectral errors and more stable performance over the frequency range and for a lower SNR of reference channels. Conversely, dual-wavelength strategies exhibit reduced accuracy, though they offer flexibility when fixed phase shifts cannot be maintained. The optimal combination of two same-wavelength reference channels, phase-shifted, is a promising configuration for spaceborne FTSs, so the development and test of an instrument breadboard is envisaged as the consequent development of this work. Full article

Railway traffic monitoring requires robust detection technologies capable of operating reliably under real-world vibration and environmental conditions. In this work, we present the design and validation of an optical vibration sensor based on a Single-mode–Multimode–Single-mode (SMS) fiber structure for Light Rail Vehicle (LRV) detection. The sensing mechanism relies on multimodal interference in the multimode fiber (MMF), where rail-induced vibrations modify the guided mode distribution and, consequently, the transmitted optical intensity. The optical signal is converted to voltage and processed through an embedded acquisition system. Additionally, we conducted tests with freight trains and maintenance trains in order to evaluate the applicability of the sensor in other types of trains besides the LRV. We conducted laboratory experiments to assess mechanical stability, sensibility, and packaging strategies, followed by supervised field tests on an operational LRV line. The recorded time-domain signal exhibited clear modulation during train passage, and first-derivative and sliding-window variance analyses were applied to reliably identify vibration events, even in the presence of slow baseline drift. In addition, frequency-domain analysis was performed by applying the Fast Fourier Transform (FFT) to the measured signal, enabling the identification of characteristic low-frequency spectral components induced by train passage. A quantitative sensitivity assessment was further carried out by correlating the integrated spectral energy (0–12 Hz) with vehicle weight, yielding a linear response with a sensitivity of 0.0017 a.u./t and coefficient of determination $R^2=0.933$. The proposed solution demonstrated stable operation using commercially available low-cost components, confirming the feasibility of SMS-based optical sensing for railway monitoring. These results indicate strong potential for future deployment in traffic safety systems and distributed sensing networks. Full article

Steels remain among the most widely used structural and engineering materials in modern infrastructure, energy systems, and industrial facilities. Their long-term reliability depends critically on the early detection of corrosion damage and microstructural degradation. This review surveys recent advances in two complementary families of non-destructive evaluation (NDE) methods: magnetic techniques, including magnetic Barkhausen noise (MBN), magnetic flux leakage (MFL), eddy current testing (ECT), and magnetic hysteresis analysis; and electrochemical methods including electrochemical impedance spectroscopy (EIS), linear polarization resistance (LPR), scanning vibrating electrode technique (SVET), and electrochemical noise (EN). Recent progress in sensor miniaturization, signal processing algorithms, and multi-technique integration is reviewed. Particular attention is given to the sensitivity of these methods to microstructural changes reported in the literature, including carbide dissolution, phase transformations, temper embrittlement, and sensitization in stainless steels, as well as to the conditions under which such sensitivity has been demonstrated. The potential synergy between magnetic and electrochemical monitoring is discussed as a possible pathway toward more robust, condition-based maintenance frameworks. Challenges related to field deployment, environmental interference, calibration, and data interpretation are identified, and future directions—including machine learning-assisted analysis and multi-physics sensor arrays—are outlined. Full article

This study analyzes how wind-induced vibrations can be exploited to harvest energy for powering remote weather stations. Three kinds of wind-induced vibrations are considered: vortex-induced vibrations, galloping, and flutter. Experimental tests on prototypes and numerical results show that the galloping harvester is the solution most suited to the proposed application. The numerical model makes it possible to simulate both T- and I-shaped harvesters and to analyze the effect of variations in the main design parameters: bluff-body mass, cantilever stiffness, and damping. Experimental tests show that a galloping energy harvester can supply an average power close to the average electrical load of an IoT wireless sensor for environmental monitoring, without requiring an additional battery supply. 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

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

The growth of human activity in cities is a key factor in the degradation of air quality. Numerous studies have demonstrated the link between air quality and the existence of dangerous and chronic diseases that are extremely costly for individuals and society. This study presents an analytical framework that compares fixed and mobile air-quality monitoring approaches in cities with limited resources, using Mohammedia city, Morocco, as an example. The framework centers on mobile monitoring units mounted on vehicles and equipped with affordable sensors, GPS technology, and wireless communication systems to track important pollutants, including fine particulate matter (PM_2.5 and PM₁₀) and harmful gaseous compounds (NO₂, SO₂, CO, O₃). The evaluation relies on scenario-based modeling, performance data from existing literature, and calculations of costs throughout the system’s lifetime. To enhance measurement reliability, the researchers developed a correction system that addresses measurement errors caused by temperature, humidity, vehicle speed, vibrations, traffic-related interference, operational interruptions, and communication limitations. The findings indicate that fixed monitoring stations deliver superior measurement precision, with estimated uncertainty ranging from ±1.2–2.5%, though their coverage area is restricted to 0.534 km² (representing 1.6% of Mohammedia). In comparison, the suggested mobile setup could potentially monitor 9.8 km², covering approximately 30% of the city, while decreasing infrastructure needs and setup time (2–4 h compared to 2–4 weeks). Over 10 years, the total cost is EUR 252,000 for mobile monitoring, compared with EUR 3.6 million for a network of 20 fixed stations. These results demonstrate that corrected mobile monitoring systems offer significant promise as an economical and sustainable approach for managing urban environmental conditions. Full article

Bolted joints are extensively used in a wide range of industrial and commercial structures, making their condition monitoring essential for ensuring structural integrity and operational safety. Under the influence of vibration, cyclic loading, and environmental factors, bolts may gradually lose preload, which can degrade joint stiffness and eventually lead to structural failure. To address this issue, this study presents a smart percussion system developed on a Raspberry Pi platform that integrates acoustic signal acquisition, real-time signal processing, and visualization of diagnostic results. A bolt looseness detection strategy combining audio feature extraction with unsupervised learning is proposed. In contrast to traditional percussion-based approaches that depend on supervised learning and predefined baseline datasets, the proposed method does not require prior reference data, significantly improving its adaptability and ease of deployment across different structures, which shows essential practical significance. Experimental investigations demonstrate the effectiveness and advantages of the proposed system, indicating its strong potential to enhance percussion-based bolt looseness detection and to support real-time structural health monitoring, which are real-world engineering applications. Full article

The European Union’s regulatory mandate requirements for vehicular components include the integrity of sealing performance, mitigating leaks from fuel tanks and transmission systems in order to guard against environmental pollution. Non-compliance can result in significant costs for the OEM and their supplier base. The majority of the reported research regarding leakage from radial lip seals focuses on static analysis of leakage under a given set of laboratory conditions. However, in practice, seal conjunctions are often subjected to significant excitations due to vehicular vibration. In the current study, the case of a front-wheel drive vehicle, equipped with three-axle accelerometers and subjected to a comprehensive road test, is used as the basis for the development of a realistic representative test rig. The test rig is developed using bespoke components from the vehicle under investigation to assess the impact of the encountered natural frequencies on sealing performance in controlled laboratory conditions, when the system is subjected to controlled excitation. Experiments are conducted to evaluate leakage at the transmission interface, focusing specifically on the sealing system’s performance. The influence of driveshaft manufacturing processes using corundum grinding and subsequent surface topography upon leakage performance are also considered. Identified modal response frequencies are imposed upon the test rig using a shaker, whilst the seal leakage is measured. The importance of shaft roughness characteristics, such as topographical skewness upon seal leakage rate under various resonant conditions, are ascertained. The results indicate potentially significant leakage rates under excitation conditions, with a non-optimised shaft roughness profile. Full article

This study proposes an exploratory framework for identifying stress locations during travel with low-speed mobility devices (LMDs), such as electric wheelchairs. In this framework, stress factors perceived during LMD travel were identified through a post-ride questionnaire, and the travel route was divided into 100 m segments to enable location-specific stress evaluation. The identified factors were quantified using environmental data to construct an environment-based stress estimation index. Based on these quantified factors, a Composite Stress Score (CSS) was calculated to estimate stress levels along the route. Experiments with healthy adult participants were conducted to examine the feasibility of the proposed method. The results identified poor road surface conditions and vibrations, encounters with other road users, and narrow sidewalks as key stress factors during LMD travel. To examine whether the proposed method captures stress-related responses, correlations between CSS-based stress estimates and heart rate variability (HRV) indices were analyzed. The results showed that CSS calculated from poor road surface/vibrations, encounters with other road users, and narrow sidewalks exhibited moderate negative correlations with SDNN, suggesting that higher CSS values may correspond to increased physiological stress responses. These findings provide preliminary support for the exploratory feasibility of estimating potential stress locations during LMD travel using environmental data. However, the generalizability of the results is limited due to the specific experimental route and the use of healthy adult participants. Full article

Leveraging differential interferometric techniques, ground-based synthetic aperture radar (GB-SAR) delivers highly accurate displacement measurements, typically reaching submillimeter scales. However, in practical engineering, minor platform instability induced by environmental factors gives rise to space-variant baseline errors, which affects the deformation value. In response to this issue, this paper presents a method combining Taylor expansion and singular value decomposition for estimation and compensation of the space-variant baseline error. Initially, the Gaussian Mixture Model (GMM) is employed to adaptively select high-quality Permanent Scatterers (PSs) to facilitate robust data provision for the following error parameter estimation. Subsequently, a three-dimensional multi-parameter model for the space-variant baseline error is established via Taylor expansion, followed by parameter estimation using Singular Value Decomposition (SVD). Experiments indicate that the proposed approach effectively mitigates the error phase arising from platform vibration, thereby enhancing the precision of GB-SAR deformation inversion. Full article

In the field of mechanical engineering, inclined rubber bearings reduce vertical stiffness through tilted arrangement to effectively isolate environmental vibrations. When applied to large-scale structural engineering, however, further attention must be paid to their vertical hysteretic performance under large deformation, so as to provide a basis for three-dimensional seismic isolation analysis of structures. Traditional seismic design often simplifies the vertical constitutive model of bearings as linear, while tests have shown that the vertical behavior of inclined rubber bearings exhibits significant asymmetric hysteretic characteristics, which cannot be accurately described by existing symmetric constitutive models. In this paper, vertical performance tests are further conducted on inclined rubber bearing specimens, and a modified hysteretic polynomial model is proposed to adapt it to the theoretical description of asymmetric vertical hysteretic behavior of inclined rubber bearings. Through parameter modification, device testing, and comparative analysis of results, the accuracy and effectiveness of the model are verified, providing a theoretical basis for its engineering application. Full article