Search Results (199)

This paper presents the results of numerical modeling and vibration testing of a nanosatellite’s optical payload, aimed at assessing its mechanical stability under the mechanical impacts of launch. The purpose of the study is to compare finite element modeling (FEM) data with experimental testing to refine the computational model and improve the reliability of mechanical stability predictions. The methodology included an FEM analysis with an average damping coefficient, an adapter blank test, a resonance study with a low-level sinusoidal run, random vibration tests, and a sinusoidal pulse test. The FEM results showed an average yield margin of safety MoS = 2.5 with a minimum MoS = 1.8 in the primary mirror mount area. The adapter blank test confirmed the absence of natural resonances in the operating range. The resonance study revealed modes in the 300–1340 Hz range, with the most pronounced peaks in the secondary mirror bracket (520–600 Hz) and the electronics unit (1030–1100 Hz). A comparison of the root mean square (RMS) acceleration values between calculations and tests revealed discrepancies due to the heterogeneous nature of the damping. The values of ζ determined by the half-power method varied from 0.9% to 4.8%, which confirms the dependence of the damping properties on the frequency and localization of the modes. The obtained results confirmed the structural integrity of the payload, allowed for the localization of structural elements, and substantiated the need to consider actual damping coefficients in FEM models. The presented data can be used to optimize the design and improve mechanical stability during payload integration into the satellite platform. Full article

Early train detection is vital for ensuring the safety of railway personnel, particularly in remote locations where fixed signaling infrastructure is unavailable. Unlike many existing solutions that rely on high-power, high-cost sensors and compute platforms, this work presents a lightweight, low-cost, and portable framework designed to run entirely on resource-constrained microcontrollers with just kilobytes of Random Access Memory (RAM). The proposed system uses vibration data from low-cost accelerometers and employs a simple yet effective Linear Regression (LR) model for almost real-time prediction of train arrival times. To ensure feasibility on low-end hardware, a parallel-processing framework is introduced, enabling continuous data collection, Machine Learning (ML) inference, and wireless communication with strict timing and energy constraints. The decision-making process, including data preprocessing and ML prediction, completes in under 10 ms, and alerts are transmitted via LoRa, enabling kilometer-range communication. Field tests on active railway lines confirm that the system detects approaching trains 15 s in advance with no false negatives and a small number of explainable false positives. Power characterization demonstrates that the system can operate for more than 6 days on a 10 Ah battery, with potential for months of operation using wake-on-vibration modes. Full article

Early and accurate detection of distributed bearing faults is essential to prevent equipment failures and reduce downtime in industrial environments. This study explores the optimal selection of input signal sources for high-accuracy distributed fault classification, employing wavelet transform and machine learning algorithms. The primary contribution of this work is to demonstrate that robust distributed bearing fault diagnosis can be achieved through optimal sensor fusion and wavelet-based feature engineering, without the need for deep learning or high-dimensional inputs. This approach provides interpretable, computationally efficient, and generalizable fault classification, setting it apart from most existing studies that rely on larger models or more extensive data. All experiments were conducted in a controlled laboratory environment across multiple loads and speeds. A comprehensive dataset, including three-axis vibration, stray magnetic flux, and two-phase current signals, was used to diagnose six distinct bearing fault conditions. The wavelet transform is applied to extract frequency-domain features, capturing intricate fault signatures. To identify the most effective input signal combinations, we systematically evaluated Random Forest, XGBoost, and Support Vector Machine (SVM) models. The analysis reveals that specific signal pairs significantly enhance classification accuracy. Notably, combining vibration signals with stray magnetic flux consistently achieved the highest performance across models, with Random Forest reaching perfect test accuracy (100%) and SVM showing robust results. These findings underscore the importance of optimal source selection and wavelet-transformed features for improving machine learning model performance in bearing fault classification tasks. While the results are promising, validation in real-world industrial settings is needed to fully assess the method’s practical reliability and impact on predictive maintenance systems. Full article

Feedthrough interference is inevitably introduced in MEMS gyroscopes due to non-ideal factors such as circuit layout design and fabrication processes, exerting non-negligible impacts on gyroscope performance. This study proposes a feedthrough suppression scheme for MEMS gyroscopes based on mixed-frequency excitation signals. Leveraging the quadratic relationship between excitation voltage and electrostatic force in capacitive resonators, the resonator is excited with a modulated signal at a non-resonant frequency while sensing vibration signals at the resonant frequency. This approach achieves linear excitation without requiring backend demodulation circuits, effectively separating desired signals from feedthrough interference in the frequency domain. A mixed-frequency excitation-based measurement and control system for MEMS gyroscopes is constructed. The influence of mismatch phenomena under non-ideal conditions on the control system is analyzed with corresponding solutions provided. Simulations and experiments validate the scheme’s effectiveness, demonstrating feedthrough suppression through both amplitude-frequency characteristics and scale factor perspectives. Test results confirm the scheme eliminates the zero introduced by feedthrough interference in the gyroscope’s amplitude-frequency response curve and reduces force-to-rebalanced detection scale factor fluctuations caused by frequency split variations by a factor of 21. Under this scheme, the gyroscope achieves zero-bias stability of 0.3118 °/h and angle random walk of 0.2443 °/h/√Hz. Full article

Mechanical vibrations induced by electromagnetic forces during transformer operation can lead to winding deformation or failure, an issue responsible for over 12% of all transformer faults. While previous studies have predominantly relied on accelerometers for vibration monitoring, this study explores the use of an optical sensor for real-time vibration measurement in a dry-type transformer. Experiments were conducted using a custom-designed single-phase transformer model specifically developed for laboratory testing. This experimental setup offers a unique advantage: it allows for the interchangeable simulation of healthy and deformed winding sections without causing permanent damage, enabling controlled and repeatable testing scenarios. The transformer’s secondary winding was short-circuited, and three levels of current (low, intermediate, and high) were applied to simulate varying stress conditions. Vibration displacement data were collected under load to assess mechanical responses. The primary goal was to classify this vibration data to localize potential winding deformation faults. Five supervised learning algorithms were evaluated: Random Forest, Support Vector Machine, K-Nearest Neighbors, Logistic Regression, and Decision Tree classifiers. Hyperparameter tuning was applied, and a comparative analysis among the top four models yielded average prediction accuracies of approximately 60%. These results, achieved under controlled laboratory conditions, highlight the promise of this approach for further development and future real-world applications. Overall, the combination of optical sensing and machine learning classification offers a promising pathway for proactive monitoring and localization of winding deformations, supporting early fault detection and enhanced reliability in power transformers. Full article

This study investigates the dynamic performance and degradation behavior of corrugated cardboard used as protective packaging for home appliances subjected to random vibrations during transportation. Simulated vibration tests were conducted on fully packaged refrigerators to assess the mechanical response of cardboard and expanded polystyrene (EPS) supports under prolonged vibration excitation. Relaxation tests were performed to characterize time-dependent stress decay in the absence of vibration, while cantilever beam experiments quantified dynamic stiffness degradation during vibration exposure. The vibration-induced damage was evaluated by monitoring the decrease in support stiffness over time, revealing a distinct exponential reduction that correlated with increasing excitation levels. Statistical load count analyses, based on auto-spectral methods and Basquin’s power law, were used to model fatigue behavior and predict service life. The findings demonstrated that corrugated cardboard exhibited comparable performance to EPS in maintaining support stiffness while offering the advantage of environmental sustainability. These results provide quantitative evidence supporting the use of cardboard as an effective and eco-friendly alternative to polymer-based packaging materials, contributing to the development of optimized packaging solutions with enhanced vibration durability. Full article

Ultra-high-voltage (UHV) transmission lines are prone to galloping and oscillations under ice and wind loads, posing risks to system reliability and safety. Accurate aerodynamic coefficients are essential for evaluating these effects, but conventional wind tunnel and CFD methods are costly and inefficient for practical applications. To address these challenges, this study develops a surrogate model for rapid and accurate prediction of aerodynamic coefficients for six-bundle conductors. Initially, a CFD model to calculate the aerodynamic coefficients of six-bundle conductors was proposed and validated against wind tunnel experimental results. Subsequently, Latin hypercube sampling (LHS) was employed to generate datasets covering wind speed, icing shape, icing thickness, and wind attack angle. High-throughput numerical simulations established a comprehensive aerodynamic database used to train and validate multiple tree-based surrogate models, including decision tree (DT), random forest (RF), extremely randomized trees (ERTs), gradient boosted decision tree (GBDT), and extreme gradient boosting (XGBoost). Comparative analysis revealed that the XGBoost-based model achieved the highest prediction accuracy, with an R² of 0.855 and superior generalization performance. Feature importance analysis further highlighted wind speed and icing shape as the dominant influencing factors. The results confirmed the XGBoost surrogate as the most effective among the tested models, providing a fast and reliable tool for aerodynamic prediction, vibration risk assessment, and structural optimization in UHV transmission systems. Full article

Precision agriculture applies data-driven strategies to manage spatial and temporal variability within fields, aiming to increase productivity while minimizing pressure on natural resources. As interest in smart tillage systems expands, this study explores a central question: Can tillage tools be used to measure soil compaction during regular field operations? To investigate this, vibration data measurements were collected from a cultivator shank in the northeast of Kansas using the AVDAQ system. The test field soils were Reading silt loam and Eudora–Bismarck Grove silt loams. The relationship between shank vibrations, soil moisture (measured by a Hydrosense II soil–water sensor), and soil compaction (measured by a cone penetrometer) was evaluated using machine learning models. Both XGBoost and Random Forest demonstrated strong predictive performance, with Random Forest achieving a slightly higher correlation of 93.8% compared to 93.7% for XGBoost. Statistical analysis confirmed no significant difference between predicted and measured values, validating the accuracy and reliability of both models. Overall, the results demonstrate that combining vibration data with soil moisture data as model inputs enables accurate estimation of soil compaction, providing a foundation for future in situ soil sensing, reduced tillage intensity, and more sustainable cultivation practices. Full article

Random parametric errors (RPEs) are introduced into the model establishment of a laminated composite cantilever beam (LCCB) to demonstrate the accuracy and robustness of a recurrent neural network (RNN) in predicting the chaotic vibration of a LCCB, and a comparative analysis of training performance and generalization capability is conducted with a convolutional neural network (CNN). In the process of dynamic modeling, the nonlinear dynamic system of a LCCB is established by considering RPEs. The displacement and velocity time series obtained from numerical simulation are used to train and test the RNN model. The RNN model converts the original data into a multi-step supervised learning format and normalizes it using the MinMaxScaler method. The prediction performance is comprehensively evaluated through three performance indicators: coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE). The results show that, under the condition of introducing RPEs, the RNN model still exhibits high prediction accuracy, with the maximum R² reaching 0.999984548634328, the maximum MAE being 0.075, and the maximum RMSE being 0.121. Furthermore, performing predictions at the free end of the LCCB verifies the applicability and robustness of the RNN model with respect to spatial position variations. These results fully demonstrate the accuracy and robustness of the RNN model in predicting the chaotic vibration of a LCCB. Full article

Vibration-based structural health monitoring methods have been widely applied in the field of damage identification. This paper proposes an intelligent diagnostic approach that integrates a multi-scale convolutional neural network with a multi-head attention mechanism (MSCNN-MHA) for the structural health monitoring of jacket-type offshore platforms. Through numerical simulations, acceleration response signals of three-pile and four-pile jacket platforms under random wave excitation are analyzed. Damage localization studies are conducted under simulated crack and pitting corrosion cases. Unlike previous studies that often idealize damage by weakening structural parameters or removing components, this study focuses on small-scale damage forms to better reflect real engineering conditions. To verify the noise resistance of the proposed method, noise is added to the original signals for further testing. Finally, experiments are conducted on the basic structure of the jacket-type offshore platform, simulating small-scale crack and pitting damage under sinusoidal and pulse excitation, to further evaluate the applicability of the method. Compared to previous CNN and MSCNN-based approaches, the results of this study demonstrate that the MSCNN-MHA method achieves higher accuracy in identifying and locating minor damage in jacket-type offshore platforms. Full article

As engines trend toward miniaturization, lightweight design, and higher power density, noise issues have become increasingly prominent, necessitating precise radiated noise prediction for effective noise control. This study develops a machine learning model based on surface vibration test data, which enhances the efficiency of engine noise prediction and has the potential to serve as an alternative to traditional high-cost engine noise test methods. Experiments were conducted on a four-cylinder, four-stroke diesel engine, collecting surface vibration and radiated noise data under full-load conditions (1600–3000 r/min). Five prediction models were developed using support vector regression (SVR, including linear, polynomial, and radial basis function kernels), random forest regression, and multilayer perceptron, suitable for non-anechoic environments. The models were trained on time-domain and frequency-domain vibration data, with performance evaluated using the maximum absolute error, mean absolute error, and median absolute error. The results show that polynomial kernel SVR performs best in time domain modelling, with an average relative error of 0.10 and a prediction accuracy of up to 90%, which is 16% higher than that of MLP; the model does not require Fourier transform and principal component analysis, and the computational overhead is low, but it needs to collect data from multiple measurement points. The linear kernel SVR works best in frequency domain modelling, with an average relative error of 0.18 and a prediction accuracy of about 82%, which is suitable for single-point measurement scenarios with moderate accuracy requirements. Analysis of measurement points indicates optimal performance using data from the engine top between cylinders 3 and 4. This approach reduces reliance on costly anechoic facilities, providing practical value for noise control and design optimization. Full article

In order to solve the problem of random noise in rolling bearing vibration signals under complex working conditions, this paper use a symmetry VMD theory to set up a rolling bearing vibration signal noise reduction processing algorithm using the fusion HPO-VMD and improved wavelet threshold. Based on the theory of variational mode decomposition (VMD), we introduce the hunter–prey optimization (HPO) algorithm to optimize the core parameters of VMD with the minimum envelope entropy as the objective function and obtain the optimal decomposition modes that contain the rolling bearing vibration signal. And then, we propose to use an improved wavelet threshold processing method to denoise the decomposed rolling bearing vibration signal to improve the recognition effect. Through the acquisition and test of the rolling bearing vibration signal, the proposed algorithm is verified; the results show that the method can reduce random noise and avoid the information loss caused by excessive noise reduction and improve the signal-to-noise ratio. Full article

Communication via optical fiber is increasingly being used in harsh applications where environmental vibration is present. This study involves a Weibull reliability analysis focused on the performance of fiber optic connectors when they are subjected to mechanical random vibration stress to simulate real-world operating conditions, and the insertion loss (IL) degradation is measurable. By analyzing the testing times and stress levels, the Weibull shape ($\beta$) and scale ($\eta$) parameters are estimated directly from the maximal and minimal principal IL stresses (${\sigma }_1$, ${\sigma }_2$), enabling the prediction of the connector’s reliability with efficiency. The sample size n is derived from the desired reliability (R(t)), and the GR-326 mechanical vibration test (2.306 Grms for six hours) is performed on optical SC angled physical contact (PC) polish fiber endface connectors that are monitored during testing to evaluate the IL transient change in the optical transmission. The method is verified by an experiment performed with ${\sigma }_1=0.3960$ and ${\sigma }_2=0.1910$ where the IL measurements are captured with an Agilent N7745A source-detector optical equipment, and the Weibull statistical results provide a connector’s reliability R(t) = 0.8474, with a characteristic value of $\eta$ = 0.2750 dB and $\beta$ = 3. Finally, the connector’s reliability is as worthy of attention as the telecommunication sign conditions. Full article

The inter-shaft bearing is an important component of aero-engine rotor systems. It works between a high-pressure rotor and a low-pressure rotor. Effective fault diagnosis of it is significant for an aero-engine. The casing vibration signals can promptly and intuitively reflect changes in the operational health status of an aero-engine’s support system. However, affected by a complex vibration transmission path and vibration of the dual-rotor, the intrinsic vibration information of the inter-shaft bearing is faced with strong noise and a dual-frequency excitation problem. This excitation is caused by the wide span of vibration source frequency distribution that results from the quite different rotational speeds of the high-pressure rotor and low-pressure rotor. Consequently, most existing fault diagnosis methods cannot effectively extract inter-shaft bearing characteristic frequency information from the casing signal. To solve this problem, this paper proposed the denoised improved envelope spectrum (DIES) method. First, an improved envelope spectrum generated by a spectrum subtraction method is proposed. This method is applied to solve the multi-source interference with wide-band distribution problem under dual-frequency excitation. Then, an improved adaptive-thresholding approach is subsequently applied to the resultant subtracted spectrum, so as to eliminate the influence of random noise in the spectrum. An experiment on a public run-to-failure bearing dataset validates that the proposed method can effectively extract an incipient bearing fault characteristic frequency (FCF) from strong background noise. Furthermore, the experiment on the inter-shaft bearing of an aero-engine test platform validates the effectiveness and superiority of the proposed DIES method. The experimental results demonstrate that this proposed method can clearly extract fault-related information from dual-frequency excitation interference. Even amid strong background noise, it precisely reveals the inter-shaft bearing’s fault-related spectral components. Full article

Duplex ball bearings are common components in space satellite mechanisms, and their behaviour impacts the overall performance and reliability of these systems. During rocket launches, these bearings suffer high vibrational loads, making their dynamic response essential for their survival. To predict the dynamic behaviour under vibration, simulations and experimental tests are performed. However, published models for space applications fail to capture the variations observed in test responses. This study presents a multi-degree-of-freedom nonlinear multibody model of a hard-preloaded duplex space ball bearing, particularized for this work to the case in which the outer ring is attached to a shaker and the inner ring to a test dummy mass. The model incorporates the Hunt and Crossley contact damping formulation and employs quaternions to accurately represent rotational dynamics. The simulated model response is validated against previously published axial test data, and its response under step, sine, and random excitations is analysed both in the case of radial and axial excitation. The results reveal key insights into frequency evolution, stress distribution, gapping phenomena, and response amplification, providing a deeper understanding of the dynamic performance of space-grade ball bearings. Full article