Search Results (4,934)

Due to the high level of innovation involved, and the requirements arising from a new environment, the use of a quasi-direct drive motor for space applications presents not only several challenges, but also great opportunities. Such a motor is particularly well-suited to dynamic applications like walking robots or robotic arms. To ensure that it can withstand the environmental challenges, the motor must undergo extensive testing. This paper briefly outlines the development of such a motor based on prior prototypes with different design concepts. It addresses the specific requirements of a space variant and describes the selected final design. Additionally, the development of corresponding motor electronics is described. Finally, the results of a test campaign are presented. The campaign included internal functional tests to characterize the motor and external environmental tests necessary for space qualification. These tests included vibration, thermal vacuum chamber (TVAC) and electromagnetic compatibility (EMC) tests. Together, they showcased a highly dynamic motor with an efficiency of up to 90% and moved it towards a technology readiness level (TRL) of 5. Full article

This study presents experimental modal analysis of an ultra-lightweight composite structure representative of UAV application and to evaluate the suitability of different testing approaches for reliable identification of its dynamics characteristics. The investigated structure is a winglet made of carbon fiber reinforced polymer (CFRP) with a lightweight foam core. The experiment was based on impact hammer excitation combined with triaxial accelerometer measurements. Modal tests were performed under three different boundary conditions: free–free suspension using elastic cords, free–free approximation using compliant foam support, and fixed conditions reflecting the operational mounting of the winglet. The results confirm that boundary conditions constitute the dominant factor governing the dynamic response. Transition from free–free to fixed support shifted the dominant bending modal frequency from 331.5 Hz (single-sided response) and 329.9 Hz (double-sided response) 421.2 Hz in the fixed configuration, demonstrating a frequency increase of nearly 27%. Reciprocity and double-sided measurements revealed measurable frequency deviations (e.g., 116.3 Hz to 117.6 Hz) attributed to accelerometer mass loading and geometric misalignment. The 1 g triaxial accelerometer mass was shown to be non-negligible relative to the modal mass of the structure, producing observable shifts in higher-order modes. Full article

Accurate classification of spindle rotational errors is critical for ensuring machining precision and operational reliability of CNC machine tools. However, existing methods face challenges in extracting discriminative feature information from vibration signals due to small inter-class differences and complex electromechanical interference. This paper proposes a novel deep learning model, MFAFENet, based on multi-sensor collaboration and multi-scale feature information adaptive fusion. Vibration signals from three mounting positions are transformed into time-frequency information representations via Short-time Fourier Transform. The proposed network adaptively fuses multi-scale feature information from parallel branches with different kernel sizes through a branch attention mechanism. An efficient channel attention module is then incorporated to recalibrate channel-wise feature responses. The cross-entropy loss function is employed to optimize the network parameters during training. Experiments on a spindle reliability test bench demonstrate that MFAFENet achieves 93.37% average test accuracy, outperforming other comparative methods. Ablation and comparative studies confirm the effectiveness of each module and the clear advantage of adaptive fusion over fixed-weight multi-scale methods. Multi-sensor fusion further improves accuracy by 7.23% over the best single-sensor setup. The proposed method establishes an effective end-to-end mapping between vibration signals and rotational errors, providing a promising solution for high-precision spindle condition monitoring. Full article

Servo electric cylinders have been widely adopted in high-performance linear drive applications such as aerospace systems, robotic servo systems, medical equipment, advanced manufacturing, precision testing, and high-end equipment due to their advantages, including high cleanliness, compact structure, high transmission efficiency, and ease of achieving precise control. However, under complex operating conditions, system performance is influenced not only by control strategies but also closely related to factors such as friction, clearance, transmission flexibility, structural vibrations, and modeling accuracy. This paper reviews mainstream control strategies and dynamic simulation methods for servo electric cylinders, providing structured analysis and systematic evaluation of representative research. In terms of control strategies, key approaches, including classical PID control, robust nonlinear control, intelligent and learning-based control, and active disturbance rejection control, are discussed, with comparative analysis of their characteristics and limitations in tracking accuracy, robustness, adaptability, and engineering feasibility. Regarding dynamic modeling and simulation, methods such as multibody dynamics, finite element analysis, rigid-flexible coupling, and multi-domain collaborative simulation are reviewed, examining their applicability in nonlinear mechanism characterization, local structural response assessment, and high-fidelity system modeling. Current research indicates that servo cylinder control is evolving from single-method improvements toward integrated and composite approaches, while dynamic modeling has progressed from low-order simplified analyses to system-level, multi-level, and high-fidelity descriptions. Existing studies still face challenges, including insufficient unified evaluation criteria, inadequate cross-method comparisons, and insufficient integration between control design and high-fidelity models. Future research should focus on enhancing control-model co-design, experimental validation under complex conditions, and system-level optimization oriented toward intelligent and high-reliability systems. Full article

Monitoring of blade vibrations in turbomachinery equipped with ferromagnetic blades is currently performed using the Blade Tip-Timing (BTT) non-contact technique. To reduce measurement uncertainty on time samples, BTT systems require measurement probes to meet high dynamic performance requirements. Anisotropic magnetoresistive (AMR) sensors have recently gained interest for this application owing to their high sensitivity to magnetic flux variations and robustness in harsh, contaminated environments. However, a thorough dynamic characterization of AMR-based BTT probes remains largely unexplored, representing a critical gap in next-generation industrial measurement systems. This work presents a custom-designed signal conditioning circuit tailored for AMR-based BTT measurements, alongside a systematic methodology for characterizing its dynamic performance. The circuit is modeled as a block diagram, from which transfer functions are derived analytically and validated experimentally, providing a rigorous and reproducible framework for probe dynamic assessment. The complete instrumentation chain is then tested on a low-speed rotor test bench in a BTT configuration. Results reveal a fundamental sensitivity–bandwidth trade-off: satisfying the cutoff frequency requirement imposed by BTT applications inherently reduces signal gain below the threshold needed to resolve individual blade-passage events. This finding isolates the key design bottleneck for AMR-based BTT probes and provides quantitative guidance for future optimization of both sensor and circuit design toward industrial tip-timing deployment. Full article

The present paper addresses the experimental measurement of vibration frequencies using an earth pressure sensor embedded in a full-scale (1:1) test structure. The vibration frequencies within the tested structure were induced by static load tests carried out at different elevation levels (corresponding to varying thicknesses of the crushed aggregate layer) in accordance with the methodology applied on German railways (DIN 18 134). The aim of the research was to verify the stress state at individual partial levels of the tested structure on the basis of the measured vibration frequencies, and to determine the depth of influence and the load dispersion angle generated by the static load test (SLT). The measured parameters also serve as input data for parallel research focused on the assessment of transition zones between railway embankments and artificial structures along railway lines. The results presented in this paper indicate that the stress induced by the SLT decreases with increasing structural thickness of the tested construction. For a structural layer thickness of 150 mm, the resulting stress corresponds to approximately 63% of the stress value (force effect) induced on a rigid circular plate (σ = 0.50 MPa), whereas for a layer thickness of 900 mm, the stress corresponds to approximately 12% of that value. The force (stress) effects of the SLT cease to act at a depth between 900 and 950 mm (only stress due to the self-weight of the overlying material was recorded), and the load dispersion angle is approximately 40°. Full article

Industrial maintenance and predictive diagnostics constitute fundamental pillars of modern manufacturing that prevent equipment failures, minimize operational downtime, and optimize maintenance costs across diverse industrial environments. Vibration-based fault classification plays an important role in industrial operations, necessitating highly sophisticated diagnostic methodologies. This research addresses these industrial imperatives through a comprehensive investigation of novel hybrid deep learning architectures for vibration-based fault classification. This study introduces a strategic integration of Quadratic Neural Networks (QNNs), which demonstrate superior non-linear feature extraction capabilities on a vibration signal compared to traditional convolutional approaches. A systematic evaluation of seven sophisticated architectures establishes a clear performance hierarchy, with QuCNN-LSTM-Transformer emerging as the optimal model achieving 99.26% average accuracy. All proposed models demonstrate excellence, with test accuracies consistently surpassing 95% across all evaluated scenarios. The data analyzed is emprical utilizing sensor data collected from an experimental rig and shows exceptional performance consistency on CWRU and HUST datasets. This investigation establishes a new paradigm in intelligent diagnostics, offering functional guidance and definitive analysis of hybrid architectures that advance industrial fault classification applications. Full article

In response to the pressing need for miniaturized MEMS microphones in wearable technology and mobile devices, and to surmount the technical limitations inherent in conventional piezoelectric microphones, which typically depend on enlarging chip dimensions or decreasing stiffness to attain low resonance frequencies, this study introduces a novel piezoelectric MEMS microphone (PMM) design predicated on a flexibility-enhanced rotating structure. The proposed design utilizes an aluminum scandium nitride (Al_0.8Sc_0.2N) piezoelectric thin film with 20% scandium doping and incorporates four equivalent sensing units formed by four curved cutting lines centrally located on the chip. This configuration employs a nested arrangement of four cantilever beams to substantially increase vibration compliance, thereby effectively lowering the natural frequency without altering the chip’s external size. Three-dimensional finite element simulations reveal that, relative to traditional triangular cantilever beam architectures, the flexibility-enhanced rotating structure reduces the natural frequency from 15.6 kHz to 13.49 kHz while enhancing sensitivity from −44.6 dB to −40 dB. The device was fabricated via a comprehensive microfabrication process and subsequently characterized within a standardized acoustic testing environment. Experimental results indicate that the microphone attains a sensitivity of −43.84 dB at 1 kHz and exhibits a first resonance frequency of 13.5 kHz, closely aligning with simulation predictions. Furthermore, the signal-to-noise ratio (SNR) reaches 58.3 dB across the full range of human-audible frequencies. By leveraging the flexibility-enhanced rotating structure, this work achieves an optimal compromise between elevated sensitivity and reduced resonance frequency within a compact form factor, thereby offering a viable technical solution for the advancement of high-performance miniature acoustic sensors. Full article

Background: Discrepancies are frequently observed between liver steatosis grading assessed by conventional B-mode ultrasonography and vibration-controlled transient elastography (VCTE) with controlled attenuation parameter (CAP). This study aimed to identify factors associated with these differences and to evaluate whether the two imaging methods provide comparable steatosis classifications. Methods: We conducted a retrospective cross-sectional observational study including 130 hospitalized patients evaluated over a two-year period who underwent laboratory testing, abdominal ultrasonography, and transient elastography. The analyzed variables included demographic characteristics, nutritional status, comorbidities, and biochemical parameters such as alanine aminotransferase (ALAT), total cholesterol, triglycerides, gamma-glutamyl transferase (GGT), and the fibrosis-4 index (FIB-4). Patients were classified into two groups: concordant steatosis grading between the two methods (n = 61) and discordant results (n = 69). Results: Concordant steatosis grading was more frequently observed in patients with serum total cholesterol > 200 mg/dL (45.9%) and FIB-4 values between 1.45–3.25 (44.2%). A trend toward higher concordance was also observed in patients with elevated triglycerides. In contrast, viral liver disease was significantly associated with discordant results (26.2%). Higher fibrosis stages assessed by VCTE (F ≥ 2) and FIB-4 values > 3.25 showed a non-significant trend toward discordance. Conclusions: Several clinical and biochemical factors influence the agreement between ultrasound and VCTE-based CAP in the assessment of hepatic steatosis. Elevated cholesterol and intermediate FIB-4 values were associated with concordant results, whereas viral liver disease was associated with discordance between the two imaging modalities. Full article

To address the high labor intensity of weight handling and the low accuracy of testing outcomes in the 125% rated-load down-running braking test for high-speed elevators, this study proposes a numerical-model-driven evaluation method for elevator braking capability based on Dynamic Ant Colony Optimization–eXtreme Gradient Boosting (DACO-XGBoost). Firstly, to overcome the limited prediction accuracy caused by insufficient measured samples during braking analysis, vibration and noise effects are both considered, and thus an equivalent dynamic analysis is conducted for no-load up-running and 125% load down-running conditions. Based on this, a simulation-data generation approach was developed to produce loaded down-running braking samples from the no-load up-running operating condition. Secondly, by combining the simulated samples generated by the above model with a limited set of measured samples, an XGBoost model optimized by a dynamic ant colony algorithm was constructed, improving the model’s ability to fit the complex nonlinear relationships in the elevator braking process. This mitigates the constraints imposed by sample scarcity and enables accurate prediction of key braking-performance parameters. Experimental results demonstrate that the proposed DACO-XGBoost substantially improves prediction accuracy. For braking distance, it decreased from 7.5453 to 0.5661 (RMSE) and from 2.7452 to 0.0370 (MAE). For slip amount, it decreased from 60.0307 to 1.2200 (RMSE) and from 7.7401 to 0.8146 (MAE), respectively. Furthermore, after comparisons with RF, GA-RF, and PSO-RF, the effectiveness of the proposed method for quantitative evaluation of braking performance in high-speed elevators was verified. Full article

Ensuring the durability and safety of modern infrastructure critically depends on the quality and strength of concrete. The Ultrasonic Pulse Velocity (UPV) method is a widely used non-destructive testing technique for evaluating concrete properties; however, factors such as aggregate size distribution, compaction methods, and surface quality can significantly influence UPV results and their correlation with compressive strength. This study investigates the effects of different aggregate sizes and an innovative vibration-assisted compaction method—developed using piezoelectric (PZT) transducers—on the mechanical, ultrasonic, and surface properties of concrete. Four distinct aggregate size distributions were employed to produce sixteen concrete specimens with constant mix proportions. Unlike conventional low-frequency, high-power vibration practices, a high-frequency (40 kHz), low-power (120 W) vibration protocol was applied through PZT elements placed within the molds to enhance compaction and reduce entrapped air. Experimental results indicated that the heaviest specimen (7.13 kg) was the medium-aggregate sample compacted using tamping and rodding methods. The highest UPV value (4143 m/s) was obtained from the coarse-aggregate specimen subjected to three minutes of vibration. In contrast, the best compressive strength performance (22.73 MPa) was observed in the medium-aggregate specimen without any vibration treatment. The findings revealed that both aggregate size and advanced vibration techniques have significant effects on the mechanical properties, ultrasonic response, and surface quality of concrete. In addition, a proof-of-concept portable surface-finishing prototype consisting of a steel plate instrumented with multiple PZT transducers was developed, and preliminary trials qualitatively suggested improved surface leveling when applied in contact with the concrete surface. Surface roughness was quantified via image processing (Light Map 150 and Specular Map 150). The rough-area fraction decreased from ~29.8% in the untreated specimen to ~4.3% after ultrasonic application, indicating a marked improvement in surface leveling and overall surface quality. The results indicate that the applied PZT vibration protocol did not improve compressive strength; in several cases, particularly under prolonged excitation, a reduction in strength was observed. In contrast, a significant improvement in surface quality was achieved, with the rough-area fraction decreasing from approximately 29.8% to 4.3%. However, due to the limited number of specimens, the findings should be interpreted as preliminary. Overall, the method appears more promising as a surface enhancement technique rather than a direct alternative to conventional compaction methods. Full article

This paper presents an adaptive noise reduction method based on Complementary Ensemble Empirical Mode Decomposition (CEEMD) to address the non-stationary characteristics and noise interference present in friction vibration signals from mechanical equipment. and friction testing machine simulation experiments. The performance of CEEMD and Ensemble Empirical Mode Decomposition (EEMD) was compared through MATLAB R2023b simulations and experiments conducted on a friction testing machine. CEEMD achieved a computational efficiency 85.6% higher than that of EEMD and effectively reduced mode aliasing. Among them, the adaptive correlation coefficient screening method performed well in signal reconstruction, and the high correlation (correlation coefficient > 0.8) between the denoised signal and the laboratory noise signal was verified using the multi-scale permutation entropy (MPE) theory, which is of great significance for early diagnosis of mechanical faults, prediction of equipment life and timely maintenance decisions. Full article

In order to study the noise reduction performance of Porous Elastic Road Surface (PERS), the vibration noise and air pumping noise has been separated from the tire–road noise through the finite element numerical simulation method. The tire–road noise model among the tire, road and surface air has been constructed by coupling of acoustic waves. The characteristics of tire–road noise under the PERS, Porous Asphalt Concrete (PAC), and Asphalt Concrete (AC) pavements have been analyzed through the modelling. The tire–road noise has also been investigated through the noise field tests. The generating process, coupling characteristics, and noise reduction performance of the vibration noise and the pumping noise of PERS pavements has been revealed. The results show that the tire–road noise was mainly generated by the vibration noise under the vehicle speed below 80 km/h. The proportion of pumping noise gradually exceeds that of vibration noise under the vehicle speed greater than 90 km/h. And the pumping noise gradually played the major role in the tire–road noise, which also increased with the increasing of vehicle speed. Comparing with AC and PAC pavements, PERS pavement exhibited the obvious advantages in noise reduction. Additionally, the reliability of the tire–road noise model has been verified through the field noise tests. It is expected that this work will serve as a reference for future research on the mechanics of the generation of tire–road noise, and try to provided theoretical support for the application of PERS. 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

Impact artificial seismic sources are gaining popularity in reflection seismic exploration. However, challenges arise due to the uncertain delay between the hammer’s acceleration and its impact on the interface, as well as the strong vibrations or pulsed magnetic fields produced during the acceleration process. These factors complicate the synchronous triggering methods typically used in traditional explosive and sledgehammer artificial seismic sources, often resulting in temporal misalignment of the acquired data. To tackle this issue, this study introduces a high-precision synchronous triggering method based on non-contact audio detection. Utilizing an STM32F4 microcontroller as the core hardware, the system collects ambient audio and extracts 39-dimensional acoustic features via Mel-frequency cepstrum coefficients (MFCC). A lightweight convolutional neural network (CNN) model is employed to accurately identify hammer impact events. Additionally, a synchronization time compensation mechanism is implemented to address system processing delays. Results from 300 field tests conducted in three environments—open ground, construction site, and mining tunnel—demonstrate that the system achieves a triggering accuracy of up to 94.6%, with compensated triggering time errors controlled within ±125 μs, thereby meeting the minimum tolerable synchronous triggering error requirement. This study significantly enhances the reliability of impact-type Artificial Seismic Source exploration data and offers insights for the application of sound recognition in engineering surveying and other related fields. Full article