Search Results (1,598)

Duct fan motors must provide high torque within limited space to maintain airflow while requiring low vibration characteristics to minimize fluid resistance caused by fan oscillation. Axial Flux Permanent Magnet Motor (AFPM) offers higher torque performance than Radial Flux Permanent Magnet Motor (RFPM) due to their large radial and short axial dimensions. In particular, the coreless AFPM structure enables superior low-vibration performance. Conventional AFPM typically employs a core-type stator, which presents manufacturing difficulties. In core-type AFPM, applying a multi-stator configuration linearly increases winding takt time in proportion to the number of stators. Conversely, a Printed Circuit Board (PCB) stator AFPM significantly reduces stator production time, making it favorable for implementing multi-stator topologies. The use of multi-stator structures enables various topological configurations depending on (1) stator placement, (2) magnetization pattern of permanent magnets, and (3) rotor arrangement—each offering specific advantages. This study evaluates and analyzes the performance of different topologies based on efficient arrangements of magnets and stators, aiming to identify the optimal structure for duct fan applications. The validity of the proposed approach and design was verified through three-dimensional finite element analysis (FEA). Full article

Rolling bearings, as essential parts of rotating machinery, face significant challenges in fault diagnosis due to limited fault samples and high noise interference, both of which reduce the effectiveness of traditional methods. To tackle this, this study proposes a fault diagnosis approach that combines Digital Twin (DT) and deep learning. First, actual bearing vibration data were collected using an experimental platform. After denoising the data, a high-fidelity digital twin system was built by integrating the bearing dynamics model with a Generative Adversarial Network (GAN), thereby effectively increasing the fault data. Next, the Wavelet Synchro-Extracting Transform (WSET) is used for high-resolution time-frequency analysis, and convolutional neural networks (CNNs) are employed to extract deep fault features adaptively. The fully connected layer of the CNN is then combined with a Least Squares Support Vector Machine (LSSVM), with key parameters optimized through an Improved Pelican Optimization Algorithm (IPOA) to improve classification accuracy significantly. Experimental results based on both simulated and publicly available datasets show that the proposed model has excellent generalizability and operational flexibility, surpassing existing deep learning-based diagnostic methods in complex industrial settings. Full article

Remaining useful life (RUL) prediction is critical for ensuring the reliability and safety of industrial equipment. In recent years, Transformer-based models have been widely employed in RUL prediction tasks for rolling bearings, owing to their superior capability in capturing global features. However, Transformers exhibit limitations in extracting local temporal features, making it challenging to fully model the degradation process. To address this issue, this paper proposes a parallel hybrid prediction approach based on Transformer and Long Short-Term Memory (LSTM) networks. The proposed method begins by applying Empirical Mode Decomposition (EMD) to the raw vibration signals of rolling bearings, decomposing them into a series of Intrinsic Mode Functions (IMFs), from which statistical features are extracted. These features are then normalized and used to construct the input dataset for the model. In the model architecture, the LSTM network is employed to capture local temporal dependencies, while the Transformer module is utilized to model long-range relationships for RUL prediction. The performance of the proposed method is evaluated using mean absolute error (MAE) and root mean square error (RMSE). Experimental validation is conducted on the PHM2012 dataset, along with generalization experiments on the XJTU-SY dataset. The results demonstrate that the proposed Transformer–LSTM approach achieves high prediction accuracy and strong generalization performance, outperforming conventional methods such as LSTM and GRU. Full article

Shape memory alloy dampers (SMADs) are widely applied in structural vibration control due to their excellent superelastic properties. However, there has been no research on the random wind-induced vibration control of transmission tower-line (TTL) systems with added SMADs. To address this gap, this paper proposes an analytical framework for the wind-induced vibration control of TTL systems with SMADs under random wind loads. An analytical model for the coupled TTL system is developed. The constitutive relationship of the SMAD is derived using the statistical linearization method, and a vibration control approach for the TTL-coupled system with SMADs is proposed. The vibration response of the TTL–SMAD system under random wind loads is derived, and an extreme response analysis framework based on the first exceedance failure criterion is established. The results show that the optimal installation scheme for the SMAD achieves a vibration reduction of more than 30%. When the damper’s stiffness coefficient is approximately 1, the SMAD effectively controls the vibrations. Moreover, a service temperature of 0 °C is found to be the optimal control temperature for the SMAD. These findings provide important references for the application of SMADs in the vibration control of TTL systems. Full article

With the rapid advancement of industrial intelligence, ensuring occupational safety has become an increasingly critical concern. Among the essential personal protective equipment (PPE), safety helmets play a vital role in preventing head injuries. There is a growing demand for real-time detection of helmet chinstrap wearing status during industrial operations. However, existing detection methods often encounter limitations such as user discomfort or potential privacy invasion. To overcome these challenges, this study proposes a non-intrusive approach for detecting the wearing state of helmet chinstraps, inspired by the mechanosensory hair arrays found on spider legs. The proposed method utilizes multiple MEMS inertial sensors to emulate the sensory functionality of spider leg hairs, thereby enabling efficient acquisition and analysis of helmet wearing states. Unlike conventional vibration-based detection techniques, posture signals reflect spatial structural characteristics; however, their integration from multiple sensors introduces increased signal complexity and background noise. To address this issue, an improved adaptive convolutional neural network (ICNN) integrated with a long short-term memory (LSTM) network is employed to classify the tightness levels of the helmet chinstrap using both single-sensor and multi-sensor data. Experimental validation was conducted based on data collected from 20 participants performing wall-climbing robot operation tasks. The results demonstrate that the proposed method achieves a high recognition accuracy of 96%. This research offers a practical, privacy-preserving, and highly effective solution for helmet-wearing status monitoring in industrial environments. Full article

Predictive maintenance is a crucial component of smart manufacturing in Industry 4.0, utilizing data from IoT sensor networks and machine learning algorithms to predict equipment failures before they happen. This proactive approach enables timely maintenance of equipment and machinery, reducing unplanned downtime, extending equipment lifespan, and enhancing overall system reliability, ultimately leading to more efficient and cost-effective operations. Conventional machinery and equipment maintenance approaches often rely on periodic manual inspections, human observations, and monitoring, which can be time-consuming, inefficient, and resource-intensive. Therefore, implementing automation through predictive models based on IoT and machine learning techniques is crucial for optimizing the maintenance of machinery and equipment. This paper aims to leverage machine learning techniques to develop predictive maintenance models, including electric motor temperature and vibration prediction, using data from established sensor networks and production data from ERP systems. The models are designed to predict potential issues within the next ten minutes, such as whether temperature or vibration levels will exceed predefined thresholds. Full article

Herein, we report a USB-powered VR-HMD prototype integrated with our 33 mm aperture varifocal liquid lenses and electronic drive components, all assembled in a conventional VR-HMD form-factor. In this volumetric-display-based VR system, a sequence of virtual images are rapidly flash-projected at different plane depths in front of the observer and are synchronized with the correct accommodations provided by the varifocal lenses for depth-matched focusing at chosen sweep frequency. This projection mechanism aids in resolving the VAC that is present in conventional fixed-depth VR. Additionally, this system can address refractive error corrections like myopia and hyperopia for prescription users and do not require any eye-tracking systems. We experimentally demonstrate these lenses can vibrate up to frequencies approaching 100 Hz and report the frequency response of the varifocal lenses and their focal characteristics in real time as a function of the drive frequency. When integrated with the prototype’s 120 fps VR display system, these lenses produce a net diopter change of 2.3 D at a sweep frequency of 45 Hz while operating at ~70% of its maximum actuation voltage. The components add a total weight of around 50 g to the off-the-shelf VR set, making it a cost-effective but lightweight minimal solution. Full article

The application of tuned mass dampers (TMDs) to high-rise buildings or slender bridges can significantly decrease the dynamical vibrations due to external excitation, such as wind or earthquake loads. However, the individual properties of a TMD such as mass, stiffness and damping have to be designed carefully with respect to the dynamical properties of the investigated structure. In real-world structures, the influence of uncertain system properties might be critical for the performance of a TMD and thus the whole structure. Therefore, the design under uncertainty of such systems is an important issue, which is addressed in the current paper. For our investigations, we consider linear single-degree-of-freedom (SDOF) systems, where analytical formulas for the deterministic design already exist, and linear multi-degree-of-freedom (MDOF) systems, where a time integration and numerical optimization algorithms are usually applied to obtain the optimal TMD parameters. If the numerical optimization should be coupled with a sampling-based uncertainty quantification method, such as Monte Carlo sampling, the design procedure would require the evaluation of a coupled double-loop approach, which is very demanding from the computation point of view. Therefore, we focus the following paper on an efficient analytical uncertainty quantification approach, which estimates the mean and scatter from a Taylor series expansion. Additionally, we introduce an efficient mode decomposition approach for MDOF systems with multiple TMDs, which estimates the maximum displacements using a modal analysis instead of a demanding time integration. Different optimal design problems are formulated as single- or multi-objective optimization tasks, where the statistical properties of the maximum displacements are considered as safety margins in the optimization goal functions. The application of numerical optimization algorithms is straightforward and not limited to specific algorithms. As numerical examples, we investigate an SDOF system with single TMD and a multi-story frame with multiple TMDs. The presented procedure might be interesting for the design process of structures, where the dynamical vibrations reach a critical threshold. Full article

This study focuses on a comprehensive analysis of the common methods for road infrastructure monitoring, as well as the perspective of various fiber-optic sensor (FOS) realization solutions in road monitoring applications. Fiber-optic sensors are a topical technology that ensures multiple advantages such as passive nature, immunity to electromagnetic interference, multiplexing capabilities, high sensitivity, and spatial resolution, as well as remote operation and multiple physical parameter monitoring, hence offering embedment potential within the road pavement structure for needed smart road solutions. The main key factors that affect FOS-based road monitoring scenarios and configurations are analyzed within this review. One such factor is technology used for optical sensing—fiber Bragg grating (FBG), Brillouin, Rayleigh, or Raman-based sensing. A descriptive comparison is made comparing typical sensitivity, spatial resolution, measurement distance, and applications. Technological approaches for monitoring physical parameters, such as strain, temperature, vibration, humidity, and pressure, as a means of assessing road infrastructure integrity and smart application integration, are also evaluated. Another critical aspect concerns spatial positioning, focusing on the point, quasi-distributed, and distributed methodologies. Lastly, the main topical FOS-based application areas are discussed, analyzed, and evaluated. Full article

A triboelectricity-enhanced piezoelectric nanogenerator (PENG) based on pressure-processed multi-electrospun polymeric layers is herein developed for efficient vibrational energy harvesting. The hybridization of piezoelectric and triboelectric mechanisms through electrospinning has been utilized to enhance electrical output by increasing contact areas and promoting alignment within piezoelectric materials. A multi-layer structure comprising alternating poly (vinylidene fluoride) (PVDF) and poly (hexamethylene adipamide) (PA 6/6) exhibits superior electrical performance. A lateral Janus configuration, providing distinct positive and negative triboelectric polarities, has further optimized device efficiency. This approach introduces a novel operational mechanism, enabling superior performance compared to conventional methods. The fiber-based architecture ensures exceptional flexibility, low weight, and a high surface-to-volume ratio, enabling enhanced energy harvesting. Experimentally, the PENG achieved an open-circuit voltage of 14.59 V, a short-circuit current of 205.7 nA, and a power density of 7.5 mW m⁻² at a resistance of 30 MΩ with a five-layer structure subjected to post-processing under pressure. A theoretical model has mathematically elucidated the output results. Long-term durability (over 345,600 cycles) has confirmed its robustness. Demonstrations of practical applications include monitoring human joint motion and respiratory activity. These results highlight the potential of the proposed triboelectricity-enhanced PENG for vibrational energy harvesting in flexible and wearable electronic systems. Full article

Background/Objectives: Spasticity is a common and disabling sequela of stroke that limits voluntary movement and functional recovery. Vibration therapy (VT) has been proposed as a non-invasive neuromodulatory intervention, but the existing studies report inconsistent outcomes due to methodological heterogeneity. This study aimed to evaluate the overall effectiveness of VT in reducing post-stroke spasticity and to identify optimal stimulation parameters via meta-analytic and meta-regression approaches. Methods: A systematic review and meta-analysis were conducted following the PRISMA 2020 guidelines. Standardized effect sizes (Hedges’ g) were calculated based on the within-group pre–post changes and compared across the groups. Meta-regression and subgroup analyses explored seven potential moderators, including the vibration frequency, amplitude, and time since stroke onset. Results: Thirteen randomized controlled trials (RCTs) involving whole-body or focal vibration interventions in stroke populations were included. Vibration therapy significantly reduced spasticity, yielding a moderate overall effect size (Hedges’ g = −0.50; 95% CI: −0.65 to −0.34; p < 0.001). The greatest treatment effects were observed when VT was applied during the late subacute to early chronic phase (6–12 months post-stroke), with low-frequency (<20 Hz) and low-amplitude (≤0.5 mm) stimulation. The frequency, amplitude, and stroke onset emerged as significant moderators (p < 0.05). Conclusions: Vibration therapy is an effective and clinically meaningful intervention for post-stroke spasticity, particularly when delivered with low-intensity parameters during the optimal recovery window. These findings support the development of individualized VT protocols and provide evidence to guide future rehabilitation strategies. Full article

This study investigates the underwater transient vibro-acoustic response of double-layered stiffened cylindrical shells through an integrated experimental-numerical approach. Initially, vibration and noise responses under transient impact loads were experimentally characterized in an anechoic water tank, establishing benchmark datasets. Subsequently, based on the theory of transient structural dynamics, a numerical framework was developed by extending the time-domain finite element/boundary element (FEM/BEM) method, enabling comprehensive analysis of the transient vibration and acoustic radiation characteristics of submerged structures. Validation through experimental-simulation comparisons confirmed the method’s accuracy and effectiveness. Key findings reveal broadband features with distinct discrete spectral peaks in both structural vibration and acoustic pressure responses under transient excitation. Systematic parametric investigations demonstrate that: (1) Reducing the load pulse width significantly amplifies vibration acceleration and sound pressure levels, while shifting acoustic energy spectra toward higher frequencies; (2) Loading position alters both vibration patterns and noise radiation characteristics. The established numerical methodology provides theoretical support for transient impact noise prediction and low-noise structural optimization in underwater vehicle design. Full article

Introduction: Amoxicillin and Ampicillin are among the most widely used antibiotics for treating bacterial infections. While traditional drug monitoring methods often face challenges relative to accuracy and analysis speed, optical-based techniques offer a promising alternative. Fourier Transform Infrared Spectroscopy (FTIR), a well-established tool, is particularly suited for this purpose. As their molecular structures and characteristic infrared absorption features are very similar, they could be difficult to differentiate using FTIR spectroscopy. Hence, chemometric analysis is important to overcome this challenge. This study introduces a novel approach to the standard methods of antibiotic detection and monitoring, leveraging the capabilities of vibrational spectroscopy and helping in antimicrobial stewardship. Attenuated Total Reflection (ATR)–FTIR is carried out with chemometric tools to investigate Amoxicillin and Ampicillin over different degradation processes. Principal Component Analysis (PCA) was used in the fingerprint region to detect differences between the studied antibiotics. Additionally, absorbance intensity in the fingerprint region was monitored to assess the degradation of each antibiotic over time. To achieve this, the area under the curve was calculated and subjected to inferential statistical tests for both intragroup (the degradation of the same antibiotic) and intergroup (degradation within the same time interval, comparing the two antibiotics) comparisons. All analyses were performed in OriginLab and using Python in the Google Colab and Orange environments. For the calculations of the limit of detection (LoD), the method based on the calibration curve was used. Through the experiments, it was possible to identify the fingerprints of each antibiotic and statistically separate them, despite both belonging to the same class of antibiotics, where the spectral peaks appear in the same region. For degradation, all tests were conducted with a significance level of α = 5%. In this investigation, our results show several quantification characteristics with a detection limit of 96.76 mM for Ampicillin and 66.01 mM for Amoxicillin using the peak intensity. This research demonstrates that FTIR spectroscopy is effective for antibiotic detection and has the potential to be further developed into a monitoring protocol. Full article

Although contact-based vibration signal methods for mechanical equipment fault diagnosis demonstrate superior performance, their practical deployment faces significant limitations. In contrast, acoustic signals offer notable deployment flexibility due to their non-contact nature. However, acoustic diagnostic methods are susceptible to environmental noise interference, and fault samples are typically scarce, leading to insufficient model generalization capability and robustness. To address this, this paper proposes an acoustic–vibration feature fusion strategy based on heterogeneous transfer learning, further integrated with a knowledge distillation framework. By doing so, it aims to achieve efficient transfer of vibration diagnostic knowledge to acoustic models. In the proposed approach, a teacher model learns diagnostic knowledge from highly reliable vibration signals and uses this to guide the training of a student model on acoustic signals. This process significantly enhances the diagnostic capability of the acoustic-based student model. Experimental studies conducted on a custom-built test rig and public datasets demonstrate that the proposed method exhibits excellent diagnostic accuracy and robustness under unseen working conditions. Full article

To address the collaborative demand for low-frequency vibration control and energy recovery, this paper proposes a dual-functional structure integrating low-frequency vibration isolation and broadband energy harvesting. The structure consists of two core components: one is a quasi-zero stiffness (QZS) vibration isolation module composed of a linkage-horizontal spring (negative stiffness) and a vertical spring; the other is an energy-harvesting component with an array of parameter-differentiated piezoelectric cantilever beams. Aiming at the conflict between the structural dynamic stiffness approaching zero and broadening the effective working range, this paper establishes a dual-objective optimization function based on the Pareto principle on the basis of static analysis and uses the grid search method combined with actual working conditions to determine the optimal parameter combination. By establishing a multi-degree-of-freedom electromechanical coupling model, the harmonic balance method is used to derive analytical solutions, which are then verified by numerical simulations. The influence laws of external excitations and system parameters on vibration isolation and energy-harvesting performance are quantitatively analyzed. The results show that the optimized structure has an initial vibration isolation frequency below 2 Hz, with a vibration isolation rate exceeding 60% in the 3 to 5 Hz ultra-low frequency range and a minimum transmissibility of the order of 10⁻² (vibration isolation rate > 98%). The parameter-differentiated piezoelectric array effectively broadens the energy-harvesting frequency band, which coincides with the vibration isolation range. Synergistic optimization of both performances can be achieved by adjusting system damping, parameters of piezoelectric vibrators, and load resistance. This study provides a theoretical reference for the integrated design of low-frequency vibration control and energy recovery, and its engineering implementation requires further experimental verification. Full article