Search Results (2,808)

Regenerative vibrations are a major hindrance to flexible cantilever structures during milling, resulting in a reduced tool life and diminished surface finish. In this research, two actively controlled methods are directly compared: a genetic algorithm (GA)-optimized classical proportional-integral-derivative (PID) controller and a GA-optimized fractional-order PID (FOPID) controller for a milling-dependent regenerative force on a flexible cantilever beam via numerical modeling, using piezoelectric actuator/sensor patches. The original aspect lies in synergistically combining fractional-order control with genetic algorithm-based optimization to actively reduce chatter and increase the machining stability of flexible milling systems. The simulation results from the GA-FOPID controller exhibited a reduction in vibration of approximately 92.70% compared with the open-loop system by reducing the RMS value from 1.5058 × 10⁻⁴ m to 1.0996 × 10⁻⁵ m. By reducing the vibration level and enlarging the predicted stable machining region, these improvements could potentially contribute to longer tool life, improved surface finish, and reduced post-processing requirements, although these technological benefits were not directly modeled in the present study. The main innovation of this work involves a unique combination of fractional-order control, PZT actuation, and genetic algorithm optimization in a regenerative milling delay architecture. To the best of the authors’ knowledge, based on the literature surveyed in this work, this combination of techniques has not previously been reported for active chatter suppression. The stability lobe diagram (SLD) analysis, conducted under the single-mode approximation that serves as the reference framework for the like-for-like comparison of the five investigated configurations, shows that the critical axial depth of cut at the representative spindle speed increases from a_p,crit(1500) = 0.061 mm for the uncontrolled system to 0.52 mm under GA-FOPID control. This enlargement of the predicted stable machining region was further confirmed, at a comparable order of magnitude, when the structural model was extended to include the two next bending modes, indicating that the trend is not an artifact of the single-mode simplification. Therefore, although the results were obtained exclusively from numerical simulation and have not yet been experimentally validated, they support the use of optimization-based methods to implement FOPID strategies as a means to increase both reliability and performance of flexible milling configurations. Full article

The reliability of vibration measurements performed during turbojet engine testing depends not only on sensor accuracy but also on the configuration of the measurement system. Frequency bandwidth is one of the configuration parameters that directly influences the global root mean square (RMS) vibration velocity used for condition monitoring and machinery assessment. This study presents a metrological evaluation of the influence of frequency bandwidth on RMS vibration measurements obtained during turbojet engine test bench operation. Measurements acquired under stabilized operating conditions were compared for three frequency bandwidths (70–1200 Hz, 125–1500 Hz and 125–2000 Hz). A comparative analysis based on the squared RMS values was performed to evaluate the differences between the investigated bandwidth configurations. The results demonstrate that bandwidth selection significantly affects the reported RMS value. Substantial differences were observed between the RMS values obtained using the investigated bandwidth configurations, particularly at the higher rotational speeds. The findings indicate that measurement configuration constitutes an important influence quantity in RMS vibration measurements and should therefore be clearly defined and documented to ensure repeatability and comparability of measurement results. Full article

This study addresses the integration of sustainability-oriented analytics and decision support in CNC milling through a hybrid information system combining structured data management, sustainability KPIs, rule-based expert reasoning, and machine learning models. The proposed framework is evaluated using the publicly available NASA Ames Milling Tool Wear Dataset. Sustainability indicators related to operational energy demand, tool degradation, and vibration/acoustic-emission response are computed from machining parameters and sensor-derived features. Random Forest and Support Vector Machine models are used for tool wear classification. The expert system applies deterministic rules to identify operational risks, which are combined with machine learning predictions through a hierarchical decision-fusion strategy. Under case-wise cross-validation, the Random Forest achieved a mean classification accuracy of 73.8%, while the Support Vector Machine achieved 68.8%. The expert system most frequently identified elevated vibration-index and acoustic-emission conditions, while critical clean-production risks occurred rarely. Overall, the results demonstrate the feasibility of integrating expert knowledge, sustainability KPIs, and data-driven models into a hybrid information system for decision support in CNC milling environments. The proposed framework provides a foundation for future research on hybrid information systems supporting sustainable manufacturing and intelligent decision making. Full article

Cross-border trade security risks are often embedded in inconsistencies among trade documents, logistics trajectories, hardware sensor states, and financial settlement activities. Existing methods primarily rely on structured declaration fields, making it difficult to verify digital declarations against actual physical processes or to generate complete evidence suitable for regulatory review. To address these challenges, TradeSense-EQA is proposed as a cross-border trade security anomaly detection and evidence-grounded English question-answering framework. Multisource sensing information, including trade documents, GPS/AIS trajectories, RFID records, electronic seal events, port weighing data, temperature and humidity measurements, vibration signals, container door states, and visual images, is jointly modeled within the framework. The reliability-aware representation module dynamically adjusts sensing-channel weights according to data missingness, sampling intervals, device health states, and communication quality. The trade-process-constrained module identifies anomalies across declaration, packing, transportation, transshipment, arrival, and customs clearance stages and generates process-consistent evidence chains. The evidence-grounded question-answering module answers English trade risk questions on the basis of verified documentary fields and sensor records, while confidence estimation and abstention mechanisms are incorporated to reduce factual hallucinations. Experimental results demonstrate that TradeSense-EQA achieved an Accuracy of $0.918$, a Precision of $0.909$, a Recall of $0.897$, a Macro-F1 of $0.903$, and a ROC-AUC of $0.958$ on the cross-border trade anomaly detection task, outperforming baseline methods including XGBoost, LightGBM, TCN, Transformer, BERT, CLIP, and VisualBERT. On the English trade risk question-answering task, Exact Match, Token-level F1, BLEU, ROUGE-L, and BERTScore reached $0.782$, $0.851$, $0.668$, $0.801$, and $0.934$, respectively. Ablation results further confirmed the effectiveness of hardware sensing input, reliability-aware weighting, declaration–fact alignment, process-graph reasoning, and evidence-constrained generation. The proposed framework provides a reliable, interpretable, and auditable artificial intelligence-driven sensing solution for customs supervision, port security, international logistics review, and trade-background investigation. Full article

Achieving high fault-classification accuracy alone does not guarantee reliable autonomous operation under varying operating conditions, raising the need to assess prediction reliability and deployment readiness. This work proposes a resilience-oriented framework for fault diagnosis under varying operating conditions, characterizing diagnostic behavior under operating-condition shifts and providing complementary information on confidence, deployability, and supervision requirements. The framework fuses multi-sensor vibration and motor current signals within a Multi-Stage architecture combining a data-driven branch (DD-MSCNN) and a physics-aware branch (PA-MSCNN) integrating order-tracking descriptors, augmented by a confidence-aware deployability assessment layer. Evaluated on the Paderborn KAT dataset across six bidirectional shifts involving speed, torque, and radial force, the results reveal that operating-condition shifts are not equivalent and that their impact is strongly direction-dependent. Physical knowledge does not systematically guarantee superior performance, highlighting the complementary roles of the two representations. To formalize these observations, the Physics Contribution Index (PCI), the Shift Directionality Index (SDI), and a four-level deployability classification are introduced, providing quantitative insights into prediction reliability and autonomous operation readiness in dynamic industrial environments. Full article

Piezoelectric transducers have been extensively investigated for their widespread use in a variety of sensing and actuation applications. Among them, cylindrical piezoelectric transducers have received less attention despite their strong potential. The aim of this work is to investigate vibration mode shape control by introducing axial and circumferential steps in the thickness of these transducers. The efficacy of introducing these steps has been investigated through ANSYS simulations and is subsequently validated using Laser Scanning Vibrometer to obtain the mode shapes experimentally. Electrical impedance measurements have also been done to obtain the resonance and anti-resonance frequencies and the effective electromechanical coupling factor. The results show that steps can control vibration through two mechanisms. (i) Circumferential steps can excite modes whose circumferential mode numbers match the number of steps. These specific modes occur within a frequency range that is entirely absent in uniform thickness transducers. (ii) Axial steps can localize and amplify the vibration amplitude for axial vibration modes. Having validated the efficacy of these steps in controlling vibration modes, some experiments were done to evaluate the effect of these steps on the performance of these transducers as acoustic actuators. The strength of the acoustic field generated inside these transducers was measured experimentally using a precision microphone and it was shown that the stepped transducers can generate around 1.5 times stronger acoustic field as compared to the uniform thickness transducer. Electrical impedance results revealed that stepped transducers had lower effective electromechanical coupling factors. However, this may suggest they can provide narrowband transducers which may have numerous precision applications. These results may suggest that employing steps could control vibration, enhance the performance of cylindrical piezoelectric transducers and improve their uptake as sensors or actuators for biomedical or food industries as well as other industrial applications. Full article

Blade-tip timing (BTT) sensor technology is widely used for non-contact blade vibration measurement. However, conventional BTT methods mainly rely on sparse time-of-arrival (TOA) information, which limits continuous sensor-domain characterization of blade vibration. To address this limitation, this paper proposes a dynamic voltage-response estimation framework combining an Elite Harris Hawks Optimization-based backpropagation neural network (EHHO-BP) with waveform mapping. Zero-speed static calibration experiments are used to establish the nonlinear relationships among blade-tip radial clearance, relative angular position, and sensor voltage, and the EHHO-BP model is employed to estimate the calibration response. Blade vibration displacement obtained from numerical models or reconstructed from BTT TOA measurements is then mapped to a continuous dynamic voltage response under a quasi-static transfer assumption. Single-harmonic and multi-harmonic simulations demonstrate the response-estimation process. In the static calibration comparison, EHHO-BP achieves a median RMSE of 8.92 mV, CVRMSE of 0.56%, MAE of 6.34 mV, and R² of 0.99982. Rotating experiments at 400, 600, 800, 1200, and 1500 rpm further evaluate the transferability of the zero-speed calibration model without retraining or speed-dependent correction. Across the tested speed range, the correlation coefficient remains between 0.9868 and 0.9989 and R² remains between 0.952 and 0.997; however, the FWHM error increases from 1.469% at 400 rpm to 22.662% at 1500 rpm. These results demonstrate the good transferability of the proposed quasi-static mapping at low-to-moderate rotational speeds while revealing a progressive deterioration in temporal waveform consistency at higher speeds. The proposed framework, therefore, provides a continuous sensor-domain representation of BTT-derived blade vibration displacement and an experimentally supported assessment of its speed-dependent applicability. Full article

Time delays are inherently present in active control systems as a consequence of sensor acquisition, communication lags, and actuator dynamics, and their impact on system behavior cannot be overlooked. This paper examines the effect of delayed displacement and speed feedback gains on the nonlinear lateral and vertical vibrational behavior of a MAGLEV vehicle subjected to aerodynamic and centrifugal forces. A delayed nonlinear dynamic model incorporating a fractional-order PD controller under aerodynamic excitation is first established for the MAGLEV system. Subsequently, the method of multiple scales is employed to derive the frequency response relationships, while the corresponding steady-state solutions are analyzed to determine system stability. The investigation further explores how the delays alter the nonlinear dynamic response. It also considers the impact of changing the value of the fractional-order parameter $\alpha$ on the vehicle’s dynamics. The results showed that increasing controller delays reduces the stability region, with displacement-feedback delays having a more pronounced effect than speed-feedback delays, while fractional-order derivatives ($0<\alpha <1$) further degrade stability; consequently, the integer-order case ($\alpha =1$) is recommended to achieve lower vibration levels and improved dynamic stability. The outcomes of this work provide valuable understanding of vibration phenomena encountered in MAGLEV systems and contribute to the development of improved control and optimization strategies for safer, smoother, and more reliable vehicle performance. Full article

As vehicle intelligence and automotive electrification advance, the extensive deployment of distributed sensing nodes for comprehensive monitoring has grown rapidly. This poses severe challenges, such as rising onboard power consumption and the inability of conventional centralized power supply systems to sustain these sensors. Triboelectric nanogenerators (TENGs), an emerging technology for energy harvesting and self-powered sensing, exhibit great potential to address the above challenges. This review systematically summarizes research on TENGs for vehicle energy harvesting and intelligent sensing, covering their fundamental working principles and applications in diverse vehicle scenarios. First, the basic principle and working modes of TENGs are described, and their suitability for complex and variable vehicle environments is evaluated. Subsequently, existing applications are categorized into three domains: vehicle vibration systems, wheel–road systems, and intelligent vehicle systems. Studies on various topics are reviewed, including vibration energy harvesting and sensing, vehicle collision monitoring, tire energy harvesting, road sensing, smart cockpits, human–machine interaction, and vehicle fluid monitoring. Emphasis is placed on their technical approaches and application prospects. Finally, the state-of-the-art research and prevailing technical bottlenecks are summarized, and potential solutions and future research perspectives are discussed. This review aims to support the reliable practical deployment of TENG technology in vehicle engineering and to provide a technical basis for energy-saving strategies and in situ sensing technologies for future intelligent vehicles. Full article

The operational reliability of shaft systems in hydropower units has become increasingly critical as these units are frequently engaged in grid regulation under new power systems. This paper presents a sensor-driven method for operational status assessment and trend prediction of Francis turbine generator unit shaft systems using vibration and swing signals. Time domain features are extracted from the sensor-acquired signals to construct a multi-dimensional quantitative index system for characterizing the operational state, and a combined Entropy Weight–Coefficient of Variation–TOPSIS model with dynamic health thresholds is established for adaptive condition assessment. To address the nonlinear and non-stationary characteristics inherent in such signals, a decomposition–prediction–reconstruction fusion framework is developed, incorporating Variational Mode Decomposition (VMD) for signal decomposition and noise reduction, iTransformer for capturing global multi-variable interactions, and Bidirectional Long Short-Term Memory (BiLSTM) for bidirectional temporal feature extraction. The hybrid model achieves a coefficient of determination R² of 0.9845 on complex vibration and swing signals, demonstrating its superior prediction capability. Based on the prediction results, health scores and dynamic thresholds are calculated to perform trend analysis and health early warning. A case study is conducted using real-world monitoring data from a 306 MW Francis turbine unit. The results demonstrate that the proposed method effectively characterizes the shaft system operational state, achieving a closed-loop integration from condition monitoring to fault diagnosis and predictive maintenance. The operational status assessment and trend prediction analyses are in good agreement with actual operating conditions, providing reliable technical support for the intelligent health management of hydropower units. Full article

To address ambiguous maneuver boundaries, background interference, and uneven multi-sensor quality in long-duration flight parameter recordings, this paper proposes an adaptive flight maneuver boundary localization algorithm that integrates spectral entropy-weighted multi-channel spectrogram fusion with attitude-constrained structural correction. Multi-channel Short-Time Fourier Transform (STFT) spectrograms are first constructed from flight parameter time series. Spectral entropy (SE) is introduced to quantify the uncertainty of each channel’s time–frequency energy distribution and is combined with the maneuver activation ratio (MAR) and the linear contrast ratio (LCR) to form objective credibility weights, thereby suppressing channels dominated by aerodynamic turbulence and high frequency structural vibration. Normal overload soft gating and logarithmic noise floor subtraction are then applied to obtain an enhanced fused spectrogram, from which candidate intervals are extracted by low band energy thresholding. Finally, roll and pitch angle steady-state priors refine the event structure through local boundary refinement, cross-segment expansion/chain merging, and semantic post-processing, recovering continuous maneuvers fragmented by instantaneous energy valleys. On the held-out test sorties (SE_018–SE_020; 61 annotated intervals), the proposed algorithm achieves Precision, Recall, and F1-scores of 0.967. On the full primary corpus of 20 sorties (461 intervals), used for ablation and sensitivity analyses, the corresponding figures are Precision 0.934, Recall 0.959, and F1 0.946, with start and end boundary mean absolute errors of 1.484 s and 1.471 s. Under the same IoU protocol, consistent superiority is observed against learning-based baselines, and an independent external set of 10 sorties yields F1 = 0.938. The results indicate that entropy-constrained multi-sensor time–frequency fusion mainly improves maneuver/background separability, whereas attitude-constrained structural correction restores the integrity of long continuous maneuvers. Full article

Air-operated double-diaphragm (AODD) pumps in industrial sludge transfer suffer from gradual performance degradation due to rheological variations and component wear, yet conventional monitoring relies on scarce labeled fault data. This paper presents an unsupervised multi-sensor framework that requires no fault labels, integrating physics-informed dual health indices, HI-P (sludge load) and HI-V (mechanical stress), with a Gaussian Mixture Model anomaly detector and a physics residual attribution module. Governing equations motivate the use of these indices from five sensors: inlet and outlet flow meters (100 Hz), an air pressure transducer (100 Hz), and inlet and outlet accelerometers (1652 Hz). Trained on one healthy baseline day (86,218 one-second windows), the Gaussian Mixture Model achieves 100% day-level classification performance on the evaluated dataset (F1 = 1.00) across 455,201 test windows from nine operating days, with window-level receiver operating characteristic area under the curve (ROC-AUC) = 0.8580 and precision–recall AUC (PR-AUC) = 0.9082. Residual attribution analytically confirms that pressure residuals drive Episode 1 (HI-P peak 3.63 times baseline, Cohen’s d = 1.70) and vibration residuals drive Episode 2 (HI-V peak 5.44 times the baseline, d = 4.10), providing empirical support for the proposed physics-informed formulation without requiring fault labels. Comparisons with four unsupervised benchmarks confirm that this is the only approach that simultaneously enables label-free operation, physics-driven features, exact attribution, real-world deployment, and perfect day-level F1. Full article

During coal mine roof fracturing drilling, vibration signals from the drilling tool can reflect both the drilling state and the structural response of the roof. However, traditional vibration sensors generally depend on batteries or wired power delivery, which hinders their long-term deployment in underground monitoring environments. To overcome this limitation, a self-powered vibration sensor featuring a wedge-shaped column array structure was developed, enabling vibration-induced electrical signal generation through the triboelectric effect. The sensor utilizes drilling-induced vibration to trigger cyclic contact–separation between the nanolayers, thereby converting vibration energy into electrical signals associated with the vibration frequency. In this way, the sensor can achieve both vibration frequency measurement and energy harvesting. The sensor was experimentally verified to enable reliable frequency detection across the 0–9 Hz range, with a measurement error of less than 3%. It also retained stable operational performance under temperatures up to 100 °C and relative humidity below 90%. Moreover, the output power reached a maximum value of 8 × 10⁻⁷ W with an external load of 10⁸ Ω. The developed sensor enables self-powered vibration frequency measurement, while its redundant vibration structure enhances operational reliability. These features make it suitable for underground coal mine drilling environments characterized by limited space and strong mechanical vibration. Full article

Cantilever roadheaders are widely used in medium-soft rock tunneling. However, conventional pick cutters suffer from high rock-breaking load, excessive energy consumption, and severe abrasion, which restrict the performance of roadheader vehicle-end intelligent control systems. Existing ultrasonic rock-breaking studies mainly focus on drilling and polycrystalline diamond compact (PDC) cutters, while calibrated EDEM simulation and experimental studies of synchronous ultrasonic-vibration-assisted pick cutter cutting remain limited. This study investigates the rock-breaking behavior of synchronous ultrasonic vibration coupled with pick cutter cutting using 21 MPa artificial rock-like specimens. A calibrated EDEM simulation model was developed based on the Hertz–Mindlin with Bonding contact model and validated by uniaxial compression and Brazilian splitting tests. Meta-particle technology was applied to analyze fragmentation characteristics. The effects of ultrasonic frequency, cutting angle, and cone angle on rock-breaking load, debris production, and specific energy consumption were investigated through simulations and experiments. An ultrasonic-assisted cutting test system equipped with force sensors was established for validation. Results show that 30 kHz ultrasonic vibration effectively reduces rock-breaking load under the investigated operating conditions. A relatively favorable parameter combination obtained from the numerical simulations consists of an ultrasonic frequency of 30 kHz, a cutting angle of 40°, and a cone angle of 60°. Compared with conventional cutting, the optimized scheme reduces average cutting load by 74.69%, increases debris yield by 54.71%, and decreases mass-specific mechanical cutting energy consumption by 83.63%. This study provides quantitative data support for roadheader vehicle-end intelligent control systems. Full article

This paper presents the results of a low-cost environmental testing campaign performed on commercial off-the-shelf components intended for aerospace applications, specifically a housekeeping system designed for suborbital launchers. These tests encompass a broader range of thermal and mechanical testing procedures than is typically reported in the literature, providing a more comprehensive assessment of the system’s robustness. The housekeeping system is subjected to sine-equivalent dynamic loads representative of launch environments expected by vehicles such as Ariane 6, VEGA, and Falcon 9 using a shaker. In addition, thermal vacuum testing is conducted to evaluate system performance under temperature and pressure conditions representative of high-altitude flight. Following each test, the system’s functionality is assessed by comparing its performance against baseline laboratory conditions using telemetry data acquired by the system; most importantly, a critical failure on telemetry data acquisition is verified, which determines the survivability of the system. The successful completion of these environmental tests demonstrates the survivability of the housekeeping system, validating its reliability and suitability for operation in suborbital launcher missions. Full article