Search Results (423)

This study proposes an attitude stabilization method based on an improved adaptive Kalman filter (AKF). The aim is to address attitude fluctuations and rollover risks in rail-based agricultural transport platforms on hilly terrain caused by slope changes, load shifts and vibrations. A dynamic model integrating the load distribution and center-of-mass migration was established, and an adaptive noise covariance mechanism was used to precisely estimate the roll and pitch angles in real time. A dual-channel proportional–integral–derivative controller was designed for automatic leveling, and a rollover risk index (RRI) was adopted for safety evaluation. Simulations revealed the ability of the improved AKF to decrease the roll estimation (RMSE) from 1.2684° to 0.8670° and the stabilization time from 0.6250 to 0.3830 s for the roll and from 0.6930 to 0.4110 s for the pitch. Under 10–30° slope disturbances, the average RRI decreased from 0.1861 to 0.1506. Field tests further demonstrated decreases in the peak roll and pitch angles from 4.8° and 4.1° to 3.1° and 2.7°, respectively, and a decrease in the average RRI from 0.203 to 0.169. The improvements in estimation accuracy, leveling performance, and operational safety under complex disturbances indicate the strong engineering potential of the proposed method. Full article

A substantial share of reinforced-concrete infrastructure assets has reached an age where deterioration mechanisms such as cracking, delamination, and voiding may develop, potentially increasing safety risks and maintenance demands. Conventional condition assessment commonly relies on localized intrusive testing (e.g., coring) and manual sounding, which can be disruptive, labor-intensive, and partly subjective. Vibration-based Non-Destructive Testing (NDT) provides an alternative by exciting the structure and evaluating changes in its dynamic response. In contrast to previous studies, which typically assess a single excitation method in isolation, this study provides a systematic side-by-side comparison of three vibration-based NDT excitation approaches: mechanical impact using a custom compressed-air impact device, acoustic excitation, and shaker excitation. All three methods were evaluated under identical measurement conditions. The vibration response is measured using Laser Doppler Vibrometry (LDV), enabling non-contact acquisition of frequency-response signatures. A custom mechanical excitation device was developed and evaluated, and the results indicate that it provides stable and repeatable excitation with good defect discrimination. Experiments on specimens with representative defect types show that mechanical impact and shaker excitation yield the most repeatable and discriminative response features, whereas acoustic excitation provides insufficient signal-to-noise ratios (SNRs) for the smallest tested specimens. Among the evaluated setups, the Qsources surface-mounted shaker and the compressed-air impact device provided the most promising laboratory results. However, the large electrodynamic shaker was used mainly as a controlled reference excitation method, and scalable field inspection would require more compact and automated excitation solutions. The goal of this work is therefore to support the development of efficient LDV-based non-contact inspection methods for safer and more reliable monitoring of reinforced-concrete infrastructure. Full article

Gearbox fault diagnosis under controlled bench-test conditions with known speed variations and noise interference remains challenging because nonstationarity, background noise, and operating-condition fluctuations can easily submerge weak localized fault features. To address this issue, this study proposes a fault diagnosis method based on a fractional-order-enhanced dual-view representation and VibrMamba–VMamba collaborative modeling. First, this study introduces a Grünwald–Letnikov fractional-order differential enhancement module with a fractional order of α = 0.6 to strengthen fault-sensitive impulsive components and improve the representation of nonstationary vibration signals. The framework then uses the enhanced signal to construct dual-view inputs: a fractional-order-enhanced one-dimensional vibration sequence and a fractional-order-enhanced synchrosqueezing transform (SST) time–frequency image. Subsequently, the framework constructs a VibrMamba temporal branch and a VMamba visual branch to extract dynamic temporal features and global structural features, respectively. Instead of using simple feature concatenation, this study designs a sample-adaptive collaborative fusion mechanism with gated weighting and cross-branch residual enhancement to integrate complementary temporal–visual representations. Bench-level experiments show that the proposed method achieves 98.90% diagnostic accuracy under clean test conditions and maintains 91.52% accuracy at −5 dB signal-to-noise ratio (SNR). These results should be interpreted as bench-level validation under controlled laboratory conditions rather than as direct evidence of field-level generalization. This framework provides a methodological solution that integrates fractional-order signal enhancement, dual-view representation, and Mamba-style collaborative state-space modeling for gearbox fault classification under controlled laboratory conditions with known speed variations and noise disturbances. Full article

This paper presents a comprehensive UAV testbed that establishes quantitative baselines for hardware vulnerability diagnosis and cyber–physical security validation by leveraging comparative flight logs from PX4-based Software-In-The-Loop (SITL) simulations and multiple real-world quadrotor missions. The testbed utilizes a unified data pipeline centered on the uORB message bus and ULog format, enabling the extraction of high-resolution telemetry, including raw Inertial Measurement Unit (IMU) data, state-estimation, and actuator control signals. Evaluated across varying environmental conditions, side-by-side time-series and statistical analyses reveal critical sim-to-real discrepancies in sensor fidelity, GPS interference, and onboard resource behavior that are often overlooked in virtual environments. Real-world data exposes hardware-induced noise, mechanical vibrations, and electromagnetic disturbances that significantly impact flight stability and system reliability. By mathematically quantifying these discrepancies (e.g., via variance and probability distribution shifts), the proposed testbed establishes a rigorous baseline for distinguishing natural physical variability from anomalous or adversarial behavior. Ultimately, this work provides a foundational framework for developing robust anomaly detection models and validating the cyber–physical security of autonomous UAV systems in safety-critical environments. Full article

An Electro-Hydrostatic Actuator (EHA) constitutes a representative servo motor-driven control system, where motor torque ripple stands as a dominant contributor to electromagnetic noise and torsional vibration. Consequently, the suppression of torque ripple represents a pivotal challenge for elevating the operational performance of EHA. This work first investigates the fundamental operating principle of EHA and develops a model to characterize the origins of torque ripple. Building upon this model, a current harmonic analysis is conducted, and a harmonic injection strategy is employed to eliminate harmonic components within the EHA current during operation, thereby refining the EHA current waveform. Simulation outcomes validate the efficacy of the proposed approach, which realizes successful suppression of current harmonics and torque ripple in the EHA system. Full article

This paper presents an effective approach to reducing noise and vibration levels in ultra-high-voltage (UHV) shunt reactors based on structural optimization and damping techniques. The main sources of vibration are associated with magnetostriction of electrical steel and electromagnetic forces in the magnetic system, which induce structural excitation of the reactor tank. A combined numerical and experimental methodology is employed, including finite element modeling (FEM) of the reactor tank and field measurements of vibration displacement and acoustic noise. In contrast to previous studies focused primarily on material properties, this work emphasizes the role of structural modifications in controlling vibration transmission. The proposed solutions include the use of nitrile butadiene rubber (NBR) damping elements, optimization of the magnetic system geometry, and reinforcement of the tank structure using vertical and horizontal stiffeners. The FEM analysis in the frequency range of 50–150 Hz shows that the maximum displacement amplitude reaches 16.2 μm at the tank bottom and 10.5 μm at the tank walls. Experimental results confirm a reduction in vibration levels to 13 μm and a sound power level of 88 dBA, which meets regulatory requirements. The proposed approach improves the vibroacoustic performance and operational reliability of UHV reactors and can be effectively applied in the design of modern high-voltage power equipment. Full article

Hybrid transmission, as the power core of hybrid vehicles, has a whining problem which affects the driving experience seriously. It is of great engineering value to carry out Noise, Vibration, and Harshness (NVH) research. In this paper, a combined methodology of finite element simulation, multi-body dynamics analysis, and real-vehicle experiment is adopted to improve the whine of the hybrid transmission. Firstly, a finite element model of the DHT assembly is established, with the frequency deviation between modal simulation and test being less than 5%, meeting the accuracy requirements. Through real-vehicle tests in electric vehicle (EV) mode, the 8th and 24th orders are identified as the key whine orders, and the deviation between simulation and test for the noise of these relevant orders is ≤5 dB(A). The research clarifies that the coupling resonance between the local modes of the upper and lower cover plates of the DHT and the excitations of the P3 motor is the core mechanism leading to the whine, and the motor control unit (MCU) is confirmed as the main noise emission source. Notably, the weak structural stiffness of the MCU lower cover plate is the critical inducing factor. To address this, three support blocks are added at the center of the MCU lower cover plate for structural reinforcement. After optimization, the 8th-order vibration is reduced by an average of approximately 35 dB in the speed range above 3500 rpm, and the 24th-order vibration is decreased by an average of about 20 dB within the range of 1000–1500 rpm. Specifically, the 24th-order noise near 1300 rpm is reduced by around 13 dB, and the 8th-order noise above 3500 rpm is fully suppressed. The increasing trend of noise with rising speed is significantly curbed, and the overall NVH performance of the vehicle is greatly improved. Full article

Continuous cough monitoring provides valuable insights into respiratory health; however, conventional air-conduction microphones are highly susceptible to ambient noise and raise privacy concerns. This work presents BCough, a bone-conduction-based embedded AI platform for on-device cough detection, designed and evaluated on the MAX78000 neural accelerator. The system employs a skin-contact bone-conduction sensor worn on the chest to capture body vibrations transmitted through bone and tissue, detecting cough events while minimizing environmental interference. The custom prototype integrates a bone-conduction microphone, a synchronized 6-axis IMU, power management circuitry, and an embedded neural accelerator to support real-time inference and future multimodal extensions. A compact 8-bit quantized convolutional neural network was optimized for deployment on the MAX78000 and evaluated using leave-one-subject-out cross-validation on one-second cough and non-cough segments derived from a corpus of 19,955 labeled events collected from five participants under controlled conditions. The deployed model achieved 0.80 Macro-F1, 0.81 balanced accuracy, 0.74 cough F1, and 0.89 AUC, with 15–16 ms inference latency and approximately 20 $\mathsf{\mu }$J energy per inference on chip. These results demonstrate the feasibility of low-power, privacy-preserving, bone-conduction cough detection on embedded AI hardware within an initial five-participant study. The current design is a benchtop prototype; the findings should therefore be interpreted as an initial feasibility assessment rather than evidence of robust performance across diverse users and real-world conditions. Future work will extend this platform toward miniaturized wearable implementations combining bone-conduction and inertial sensing for continuous multimodal respiratory monitoring. Full article

Taking a metro over-track TOD (Transit-Oriented Development) project in Chongqing as the engineering background, this study adopts a combined research approach integrating field measurements and numerical simulation. A coupled finite element model of the train–track–tunnel–soil–building system and a regional acoustic model are established to systematically reveal the vibration response characteristics of building clusters above the depot induced by metro operation, the propagation mechanism of structure-borne secondary noise, and the distribution patterns of the regional acoustic environment, while identifying the areas where vibration and noise exceed the prescribed limits as well as the key influencing factors. On this basis, following a hierarchical mitigation strategy consisting of source control, path interruption, and receiver protection, an integrated control scheme is proposed through the coordinated application of track vibration reduction, building vibration isolation, acoustic environment optimization, and building sound insulation. The engineering applicability and control effectiveness of the proposed scheme are further verified by numerical simulation. The findings of this study can provide theoretical support and technical reference for the refined design and integrated prevention and control of vibration and acoustic environments in similar metro over-track development projects. Full article

Electric vehicles (EVs) have fundamentally changed the noise, vibration, and harshness (NVH) landscape of automotive powertrains. In the absence of masking internal-combustion-engine noise, harmonic components such as gear whine, electric-motor orders, and inverter-related tones become more perceptible and more critical to vehicle refinement. This review synthesizes the current state of the art in harmonic NVH engineering for electric drivetrains, focusing on the interactions between gear geometry, manufacturing variability, electromechanical coupling, structural transfer, and human sound perception. Classical mechanisms of gear-mesh excitation are revisited together with emerging EV-specific challenges, including long-wavelength flank deviations, ghost orders, lightweight housing dynamics, and psychoacoustic sound-quality requirements. The review further examines recent progress in predictive and data-driven approaches, including machine-learning-based gear-noise modeling, digital-twin concepts, and virtual NVH assessment workflows. Overall, the literature shows that harmonic NVH engineering in EVs is evolving from a conventional gear-noise problem into a multidisciplinary system-level task integrating gear dynamics, manufacturing science, structural acoustics, electric-drive control, psychoacoustics, and data-driven optimization. This review provides a structured synthesis of these developments and identifies key research gaps and future directions for the next generation of refined electric drivetrains. Full article

Class II biological safety cabinets (BSCs) are designed to protect the user, the product, and the laboratory environment by maintaining HEPA-filtered airflow; however, their fans, alarms, and structural resonances introduce acoustic and vibrational stimuli that may confound mechanosensitive cell-culture assays. In this study, we characterized the vibroacoustic environment of a cell-culture laboratory and a Class II BSC, selected representative tray locations based on measured and modeled stimuli, and evaluated in vitro wound closure in HaCaT keratinocytes using a scratch assay under alarm-induced acoustic exposure. Wound closure after 24 h was quantified using a relative area-closure metric defined as one minus the ratio of wound area at 24 h to wound area at 0 h. For each biological replicate (one flask and one scratch), two non-overlapping image regions were treated as technical subsamples and averaged to obtain a single flask-level value. Three independent experimental runs were performed, each including one flask per tray point, yielding n equals 3 independent flasks per tray point. Mean wound closure values were 73.7 percent plus or minus 15.6 percent, 75.6 percent plus or minus 7.2 percent, and 79.4 percent plus or minus 14.8 percent for tray points P1, P5, and P6, respectively (mean plus or minus standard deviation). No statistically significant differences were detected among points (one-way ANOVA on flask-level values, F equals 0.15, p equals 0.86). These findings highlight that BSC-associated acoustic and vibration stimuli should be documented when interpreting scratch-assay outcomes and motivate larger, sham-controlled studies to resolve small effect sizes relevant for assay reproducibility. Full article

Postural control is essential for daily function, and while stochastic resonance (SR) enhances balance in clinical populations, its efficacy in healthy young people remains underexplored. This study investigated (1) biomechanical effects of multisite plantar vibration on postural stability using center-of-pressure (CoP) parameters, and (2) short-term and sustained effects on balance performances. Phase 1 enrolled six participants to identify the optimal plantar stimulation configuration and to evaluate acute electromyographic responses under threshold-level vibration. Phase 2 evaluated long-term efficacy through an eight-week sham-controlled parallel-group randomized controlled trial. In this trial, eight participants received vibration combined with balance training, and another eight participants completed the same training protocol using sham insoles without vibration, analyzing CoP parameters (95% ellipse area, path length) and muscle activation (tibialis anterior, medial gastrocnemius, peroneus longus, extensor digitorum longus). Results showed full-site vibration reduced CoP area versus control (265.66 ± 188.6 mm² vs. 437.84 ± 190.95 mm², p < 0.05) without altering ankle muscle activation (all p > 0.05). Longitudinal analysis revealed CoP area reduction (−4.88 ± 10.42%) in the intervention group versus sham (p < 0.001), with maximum anterior displacement increasing by 25.03% during vibration (p < 0.05). Plantar white-noise vibration modulates CoP oscillations without neuromuscular activation changes, demonstrating that full-site stimulation acutely enhances postural stability while sustained intervention improves dynamic balance control. Full article

The mathematical characterization of non-stationary signals remains a significant challenge, particularly when impulsive components are obscured by high-dimensional noise and structural coupling. This paper proposes an application-driven mathematical methodology for a learnable discrete wavelet transform (LDWT) that combines classical multi-resolution analysis with task-optimized data-driven adaptivity. Rather than introducing entirely new foundational theory, our approach strategically relaxes constraints from orthogonal wavelet theory within the non-perfect reconstruction filter bank framework, enabling controlled spectral decomposition optimized for supervised fault diagnosis. We introduce a specialized regularization term based on the half-band property to ensure spectral complementarity and minimize cross-band correlation, while a Jacobian-based stabilization approach is formulated to ensure the convergence of filter coefficients during optimization. The proposed algorithmic architecture, LDBRFnet, features a dual-branch encoder system designed to capture the mathematical synergy between sub-band-level global statistics and time-domain local morphology. This dual-view representation effectively mitigates feature leakage and overconfidence in classification. Theoretical analysis and numerical experiments demonstrate that the learned filters satisfy the frequency-shift property and maintain robust spectral partitioning even under low signal-to-noise ratios. Validation on complex vibration datasets confirms that the framework achieves superior diagnostic accuracy (over 95.5%) and computational efficiency, reducing model parameters by 96.7% compared to state-of-the-art baselines. This work provides a generalizable mathematical approach for adaptive signal decomposition and robust pattern recognition in interdisciplinary applications. Full article

Sensing audio using non-acoustic modalities such as millimeter-wave radar and laser-based systems has emerged as an active research area with significant implications for privacy, security, and robust speech processing. These approaches recover speech-related information from vibration measurements captured by non-acoustic sensing modalities. Prior work spans a wide range of techniques, from classical signal-processing pipelines to modern machine-learning and deep-learning models, enabling applications such as speech reconstruction, eavesdropping, automatic speech recognition, and noise-robust enhancement. Some systems rely on radar or laser sensing as a standalone audio surrogate, while others fuse radar-derived features with microphone signals to improve robustness in noisy or non-line-of-sight environments. Experimental results across the literature demonstrate that recovering intelligible speech or discriminative speech features from radar or laser-sensed vibrations is feasible under controlled conditions. However, performance remains sensitive to practical factors including sensing distance, object material and geometries, environmental interference, multipath effects, and task complexity. Not all speech-related tasks are reliably solved, particularly in unconstrained real-world scenarios. Overall, the field is rapidly evolving, with open challenges in robustness, generalization, and deployment, offering several promising directions for future research. Full article

Search coil magnetometers (SCMs) are widely used in space science missions to measure time-varying magnetic fields. However, conventional SCM designs often increase sensor mass and electronic power consumption in order to meet mission-specific sensitivity requirements. This study presents the design and ground-based test results of a space search coil magnetometer (SSCM) concept aimed at reducing sensor mass and electronic power consumption while maintaining practical system operability for platform-constrained missions. Mass reduction was achieved by adopting a rolling-sheet core configuration. In addition, printed circuit board (PCB)-based interconnections between segmented windings were implemented to improve the reproducibility of assembly and mechanical robustness without additional structural complexity. Power reduction was achieved by employing an application-specific integrated circuit (ASIC)-based sensor amplifier and a compact control electronic unit implemented as a modular stack with a 1U CubeSat standard board form factor. Performance tests confirmed the stable operation of the integrated sensor–electronics chain over the target measurement band. The system-level noise-equivalent magnetic induction (NEMI) measured under laboratory conditions was 33 fT/√Hz at 1 kHz. Environmental tests including vibration and thermal cycling were performed to further verify the structural safety and functional stability of the sensor assembly under space-relevant conditions. The proposed SSCM architecture provides a practical approach for implementing low-mass and low-power magnetic field instruments for platform-constrained space missions. Full article