Search Results (186)

Multiphase swirling flows in confined spaces induce highly destructive, nonlinear fluid–structure interaction (FSI) vibrations. Understanding the underlying physical mechanisms is critical for ensuring the safety of industrial operations. This study proposes a mesoscopic multiscale framework coupling the Multi-Relaxation Time Lattice Boltzmann Method with Large Eddy Simulation (MRT-LBM-LES) and the Flügge thin-walled cylindrical shell equations to analyze two-way FSI responses. Variational Mode Decomposition (VMD) and the Hilbert–Huang Transform (HHT) are employed to decouple non-stationary broadband excitation signals. The macroscopic topological evolution of the swirling air core—from initial depression to critical breakthrough—is accurately captured. Dynamic mapping reveals a strict time-domain phase-locking mechanism between macroscopic flow instability and microscopic high-frequency structural excitation caused by cavitation bubble collapse. Furthermore, a dimensionless cross-scale energy cascade index is defined to quantify energy transfer. Results indicate that while higher discharge flow rates delay the critical breakthrough, they trigger a delayed, high-amplitude step mutation in the energy cascade, amplifying the global cumulative excitation energy by nearly 75%. Notably, the dominant high-frequency excitation consistently converges within a narrow band of 760 Hz to 790 Hz, independent of flow rate variations. These findings provide a theoretical foundation for unsteady excitation source localization and targeted vibration reduction in complex industrial pipeline networks. Full article

As urban underground spaces and infrastructure development accelerate, subsurface elements such as buried pipelines, integrated utility tunnels, subway tunnels, cavity defects, and deep-seated hidden hazards become increasingly intertwined. Consequently, urban target detection is characterized by pronounced scale discrepancies, intense environmental interference, and severely confined operational spaces. The transient electromagnetic method (TEM) is highly valuable for rapid surveys and hazard identification in urban underground spaces owing to its inherent advantages, including non-contact operation, adaptability to hardened pavements, high sensitivity to low-resistivity anomalies, and the ability to probe a broad range of depths. In recent years, research has shifted from improving isolated instrumentation to synergistically optimizing sensing platforms, transmitter–receiver systems, anti-interference methodologies, and imaging interpretation workflows. Specifically, small-loop configurations and high-frequency excitation technologies have improved shallow-sounding capabilities in confined urban spaces; anti-interference techniques have increased data reliability in complex noise environments; and apparent resistivity mapping, virtual wave-field migration, and rapid inversion methodologies have enabled profiling results to transition from qualitative identification to fine-scale interpretation. Concurrently, the evolution of ground-towed, UAV-borne, helicopter-borne, and semi-airborne platforms has progressively endowed urban TEM profiling with continuous, mobile, and scenario-specific operational capabilities. Looking to the future, further technical breakthroughs in urban TEM technology are required to improve shallow-resolution, deep-seated penetration, multi-source interference decoupling, and real-time concurrent imaging. Full article

To address the broadband noise distribution, complex excitation sources, and insufficient low-frequency attenuation of conventional porous acoustic packages in commercial vehicle cabs, this study proposes a PU–six-cavity Helmholtz metamaterial composite acoustic package for broadband noise reduction. The proposed structure consists of a 30 mm PU porous layer for mid-to-high-frequency dissipation and a 30 mm six-cavity Helmholtz metamaterial layer for low-frequency absorption, forming a 60 mm composite acoustic package. A full-vehicle acoustic model of a commercial vehicle cab was established in VA One to identify the A-weighted sound pressure level (SPL) spectrum at the driver position. The results show that the PU porous acoustic package improves the mid- and high-frequency noise response, whereas pronounced peaks remain in the low- and low-to-mid-frequency ranges. To enhance these bands, the six sub-cavities of the Helmholtz metamaterial were tuned to 200, 250, 315, 400, 500, and 630 Hz through spatial partitioning and cavity grouping. COMSOL (version 6.1) simulations and particle velocity distributions confirmed the multi-peak absorption mechanism and the selective excitation of the corresponding sub-cavities. The composite structure was further validated through impedance-tube measurements, full-vehicle acoustic simulations, and in-vehicle tests. The VA One simulation shows that the total A-weighted SPL at the driver position decreases from 68.25 dB for the PU porous package to 66.01 dB after introducing the six-cavity Helmholtz metamaterial, corresponding to an additional reduction of 2.24 dB. A preliminary in-vehicle test under a stationary idling condition shows that the total A-weighted SPL near the driver’s ear decreases from 55.83 dB(A) to 54.56 dB(A). These results demonstrate that the proposed PU–six-cavity Helmholtz metamaterial composite acoustic package combines broadband porous dissipation with low-frequency resonant absorption, providing a feasible solution for broadband noise control in commercial vehicle cabs. Full article

Electronic compensation networks used in high–accuracy inductive voltage dividers (IVDs) can introduce temperature– and aging–dependent drift sources, complicating long–term uncertainty evaluation. This paper presents a 1 kV, 10–tap, compensation–free three–stage IVD that combines multi–stage excitation, a closed shielded core, and equipotential coaxial–cable windings to suppress excitation, magnetic–coupling, and capacitive errors. An integrated reference winding is embedded in the third–stage coaxial–cable winding, enabling a simplified bootstrap self–calibration procedure without an additional auxiliary IVD. At 50 Hz, the calibrated ratio error and phase displacement are within 0.08 μV/V and 0.08 μrad, respectively, for all taps, with expanded uncertainties of 0.04 μV/V and 0.06 μrad. Wideband measurements up to 3 kHz further show that the proposed IVD can serve as a voltage–ratio reference for harmonic voltage measurement and calibration. Full article

Luminescence thermometry has evolved through decades of research focused on optimising materials and on extracting temperature information from isolated spectral features such as luminescence intensity ratios, bandwidth, line shift and excited-state lifetime. Despite extensive material development, these conventional methods remain fundamentally limited by construction: only a small subset of pre-selected spectral features is exploited, while the bulk of the temperature-relevant information encoded in the full spectrum is systematically discarded. A paradigm shift is presented here: Luminescence Thermometry 2.0 (LT 2.0), implemented through the newly developed SpectraSensML platform, in which machine learning regression operates on the entire emission spectrum to deliver temperature readout. The approach is demonstrated on a Yb³⁺-doped phosphor emitting in the near-infrared biological transparency window across 100 to 700 K. Yb³⁺ is a particularly demanding case: only the single ²F_5/2 multiplet emits, and its weakly thermally coupled Stark sub-levels yield modest sensitivity under conventional intensity-ratio thermometry. A total of 27 regression algorithms drawn from four families, namely tree ensembles, physics-aware regression models, kernel and instance methods, and neural networks, are systematically benchmarked. A sensor-fusion estimator that combines the first three principal components reaches an average root-mean-square error of 0.36 K on an unseen-temperature test set, a seven-fold improvement over the best luminescence intensity ratio variant. Standard normal variate (SNV) normalisation is identified as the most effective preprocessing strategy because it isolates the band-shape deformations that encode temperature. Single-component approaches that rely on the first principal component alone are shown to be quantitatively sub-optimal: multi-component regressors that exploit the first three principal components reduce the temperature uncertainty by close to an order of magnitude. The structural reason behind the failure of decision-tree ensembles on unseen temperatures is explained: their piecewise-constant predictions cannot interpolate beyond training set-points. The open-source SpectraSensML application used to obtain the results is released alongside the manuscript to enable reproducible community benchmarks. Full article

To address the challenges in understanding the raceway failure mechanisms of bearings under dynamic radial excitations, this study proposes a vibration evolution analysis method based on multi-source data fusion and a Granger causality test. Firstly, a vertical bearing vibration test bench that can simulate the dynamic excitation in engineering practice is built, and the bearing acceleration, inner ring displacement and cage data are collected at the same time. Subsequently, the evolution law and correlation relationship of bearing vibration signals during the expansion process of bearing raceway damage were studied. Based on this, a multi-source vibration data fusion method was proposed, and the effectiveness of different data fusion schemes in characterizing raceway damage expansion was compared. Finally, the Granger causality test was applied to analyze the causal relationship between the evolution of various vibration behaviors during the damage propagation process. Research results demonstrate that under complex loading conditions during sustained operation, the “False Brinelling” indentation gradually develops into raceway surface damage. The vibration behavior of bearings exhibits distinct stage-specific characteristics under dynamic radial excitations. Notably, variations in vibration behavior amplitude and transition timing between different operational phases demonstrate significant discrepancies. Significant alterations in causal relationships between vibration behaviors were observed throughout different degradation phases. The combined approach proposed in this paper, encompassing complex load simulation, multi-source data fusion, and causal analysis, offers a new understanding of the raceway failure mechanism of bearings under real-world operating conditions. Full article

When marine chillers operate under complex marine conditions, they suffer from severe cross-equipment feature distribution shifts, scarce labeled fault samples in the target domain, industrial vibration noise mixed in sensor signals, and difficulties in accurately identifying subtle faults with varying severity levels. To tackle these issues, this paper improves upon the domain difference perception network (DDPN) and proposes a dual-hybrid attention feature discriminant domain-Adversarial network (DAFDAN) to realize intelligent fault diagnosis across different equipment and working conditions under few-shot scenarios. The proposed method constructs a dual-branch feature encoder consisting of a source domain compressor and a target domain extender to accommodate the distinct sensor dimensions of two heterogeneous chiller types. A hybrid attention module is formed by integrating squeeze-and-excitation efficient channel attention (SE-ECA, a module for screening channel-wise features) and spatial attention, which adaptively amplifies time-series features sensitive to faults and suppresses irrelevant noise. Residual connections (shortcut paths in deep neural networks to mitigate the vanishing gradient problem during deep-layer training) are introduced to optimize feature transmission. A dual-layer domain alignment framework is built with gradient reversal layers and maximum mean discrepancy (MMD). Combined with adversarial training (a training paradigm that learns domain-agnostic features through a game between a feature extractor and a domain discriminator), the framework achieves joint optimization of implicit feature confusion and explicit distance constraints. Meanwhile, a five-stage progressive training strategy is designed, which activates multiple loss functions, including weighted cross-entropy, mean square error (MSE), binary cross-entropy (BCE), and Kullback–Leibler (KL) divergence stage by stage. Class weighting and early stopping strategies are adopted to alleviate sample imbalance and model overfitting. In this paper, the public ASHRAE RP-1043 centrifugal chiller dataset is used as the source domain, and time-series measurement data collected from a self-developed laboratory marine screw chiller serves as the target domain. Verification experiments are carried out covering one normal steady-state operating condition and 15 gradient faults falling into five major categories with different severity degrees. Results from ablation experiments (controlled-variable comparative experiments that quantify the independent contribution of each component by comparing model performance with or without a specific module/loss), multi-algorithm comparisons, and confusion matrix visualization demonstrate that the cross-domain fault diagnosis accuracy of the proposed DAFDAN approaches is 100%, outperforming mainstream transfer learning algorithms such as support vector machine (SVM), deep neural network (DNN), MMD, correlation alignment (CORAL), and domain-adversarial neural network (DANN). Multiple ablation experiments verify that the three core components—hybrid attention, adversarial training, and semi-supervised learning—jointly boost the model’s diagnosis accuracy and operational stability. The loss curves of the complete five-stage training process converge smoothly. The confusion matrix reveals zero misjudgments and zero false alarms across all 16 refined operating states, enabling precise identification of subtle incipient faults of all severity levels. This study proves that DAFDAN can effectively address the pain points of few-shot cross-equipment fault diagnosis for marine chillers and provides a reliable algorithmic reference for the intelligent operation and maintenance of ship refrigeration equipment. Full article

A bidirectional tribo-dynamic coupled model for a double-helical gear transmission is established by integrating tooth surface wear, thermal elastohydrodynamic lubrication (TEHL), eccentricity error, tooth profile error, and temperature-induced deformation. The time-varying mesh stiffness and meshing impact excitation is also calculated. The proposed model distinguishes itself from previous works through the bidirectional coupling between the dynamic model and the TEHL/wear sub-models, which allows tribological evolution (wear accumulation and thermal expansion) to feed back into the vibration response—a feature absent in previous studies. Using this model, the influence of multi-source excitations on vibration characteristics is investigated, and the distributions of film thickness, pressure, temperature rise, and friction coefficient in the contact zone are obtained. The results show that eccentricity error affects vibration displacement more strongly than velocity (ratio ≈ 2:1), whereas wear influences velocity more than displacement (after 3 × 10⁶ cycles, velocity increases by 50% vs. 8.1% for displacement); temperature rise significantly increases vibration velocity while slightly decreasing displacement. The TEHL sub-model predicts that the minimum film thickness and maximum pressure occur near the pitch point, with a temperature rise of approximately 35 K, and the friction coefficient exhibits a U-shaped distribution that shifts upward with accumulated wear. Vibration response is partially validated using vibration acceleration measurements on an FZG test rig under multiple operating conditions; the model shows consistent trends with experiments (errors < 20%), though direct validation of the tribological sub-models remains for future work. Full article

Stable and uniform pesticide application is essential for precision crop protection, input-use efficiency, and the reduction of off-target losses in large-scale farming systems. Large boom sprayers are important agricultural machines for high-efficiency crop protection, but their wide and flexible booms are highly sensitive to terrain-induced excitation, chassis motion, liquid sloshing, and hydraulic nonlinearities. These disturbances can cause roll, yaw, vertical oscillation, and boom-end height variation, thereby reducing spray uniformity, increasing drift risk, and threatening operational safety. Hybrid active–passive boom suspension systems have therefore become a key enabling technology for modern precision spraying. This review summarizes research progress in the structural design, dynamic modeling, sensing, and control of boom suspension systems for large-scale agricultural sprayers. Mainstream commercial machines commonly use double-pendulum active–passive suspension architectures, in which passive components attenuate high-frequency vibration and active subsystems improve low-frequency terrain-following performance. Recent studies have advanced electro-hydraulic actuation, disturbance compensation, adaptive control, multi-sensor fusion, and field evaluation methods; however, a unified framework for coupling boom dynamics, spray quality, sensing accuracy, and whole-machine operation remains incomplete. Key challenges include rigid–flexible–hydraulic coupling, underactuated control, uncertain parameters, external disturbances, and posture estimation errors caused by boom elastic deformation and sensor noise. Future research should emphasize rigid–flexible–fluid-coupled modeling, adaptive output-feedback control with disturbance and resonance suppression, terrain-preview and multi-source perception, and coordinated chassis–boom-spray control. These developments can support more stable, efficient, and environmentally responsible spraying operations in modern precision agriculture. Full article

Agricultural machinery operates under complex field conditions involving uneven terrain, crop flow impacts, variable speed and load, dust, moisture, and multi-source structural excitation. These factors make vibration-based fault diagnosis more challenging than that of conventional rotating machinery because weak fault features are often masked by non-stationary background vibration and operating condition disturbances. This review provides a structured synthesis of vibration-based fault diagnosis for agricultural machinery, focusing on tractors, combine harvesters, harvesting machinery, and key components such as bearings, gearboxes, transmission systems, headers, threshing drums, cleaning sieves, vibrating screens, chassis, frames, and cab systems. The review first analyzes vibration sources, fault mechanisms, and signal degradation under field conditions. It then summarizes vibration sensors, data acquisition, preprocessing, time–frequency analysis, feature representation, machine learning, deep learning, transfer learning, and multi-source information fusion. Applications are reviewed from component-level diagnosis to whole-machine monitoring. Key challenges include field data scarcity, variable conditions, sensor reliability, data leakage, model generalization, edge deployment, standardization, and long-term validation. Future research should emphasise high-quality field datasets, physics-informed and explainable models, robust cross-condition diagnosis, multimodal sensing, edge intelligence, digital twins, and predictive maintenance. This review highlights the need to connect vibration mechanisms, diagnostic models, and engineering deployment requirements for reliable agricultural machinery health monitoring. Rather than treating sensors, components, algorithms, and deployment issues as separate topics, this review organizes the literature around field-specific vibration disturbances, validation evidence, deployable diagnostic requirements, and future implementation priorities. Full article

Petrophysical parameters of rocks and ores, influenced by composition, porosity, temperature, and pressure, are generally distributed uniformly or continuously in space—relatively homogeneous within individual geological units and varying smoothly across stratigraphic transition zones and contact boundaries. Based on this geological characteristic, this paper establishes a three-dimensional (3-D) piecewise linear continuous spectral parameter model to compute forward responses of apparent spectral parameters under low-frequency current excitation. The calculation follows a two-step workflow: finite-element forward simulation of multi-frequency apparent complex resistivity, followed by recursive inversion to obtain apparent spectral parameters. The subsurface medium is discretized with hexahedral meshes, with four Cole–Cole parameters (zero-frequency resistivity, chargeability, time constant, and frequency exponent) assigned to each mesh node. Linear interpolation is adopted for complex resistivity and potential within each element, ensuring piecewise linear continuity of both physical properties and simulated fields. To improve accuracy, the total complex potential is decomposed into a primary field from the source current and a secondary field from complex conductivity variations, and the corresponding boundary value problem and variational form are derived. On this basis, we implement the finite-element algorithm for 3-D piecewise linear continuous media and the recursive inversion algorithm for spectral parameters, and develop an interactive 3-D SIP forward modeling program. Comparison with analytical solutions for a continuous layered model shows good agreement, with relative errors below 1.5% for the real part and 3.8% for the imaginary part of apparent complex resistivity. Two numerical cases—a cubic anomaly in homogeneous half-space and a sandbox model—further verify the performance of the proposed method. Full article

Aero-engine safety and prognostics and health management (PHM) rely on robust vibration-based fault diagnosis. However, many deep learning studies on rotating machinery are evaluated under random train–test splits that mix hardware instances and may obscure the domain shift faced in deployment. This paper presents a protocol-driven end-to-end baseline for multi-sensor aero-engine-relevant vibration diagnosis on the HIT inter-shaft bearing benchmark. Six synchronous vibration channels are segmented into fixed-length windows, standardized using source-domain statistics, and classified by a compact 1D CNN backbone with and without squeeze-and-excitation (SE) channel attention. A deeper ResNet1D baseline is further introduced to examine whether increasing backbone capacity improves cross-bearing generalization under the same source-only training protocol. We compare random segment-level splits with bearing-level cross-splits that hold out entire bearings as unseen target domains, and we report deployment-oriented indicators including balanced accuracy, false-alarm rate (FAR), and miss rate over five random seeds. Under random splits, the compact CNN baseline reaches near-ceiling test accuracy, confirming that the benchmark is readily separable under in-domain interpolation. In contrast, cross-bearing evaluation reveals severe degradation: in the representative split, the baseline CNN accuracy collapses to approximately 15% with near-zero normal-class recall, while ResNet1D improves fault sensitivity but still retains a high FAR above 88%. Additional cross-bearing permutations further show that this degradation is not attributable to a single unfavorable source–target split. These findings indicate that, under the tested source-only backbones and protocols, distribution mismatch is a dominant bottleneck for deployment-ready cross-bearing diagnosis. The results establish a reproducible baseline for protocol-driven evaluation in aero-engine PHM and motivate future work on domain adaptation, domain generalization, calibration, and sequential decision logic. Full article

Accurate prediction of remaining useful life (RUL) for lithium-ion batteries remains a critical yet complex challenge due to highly non-linear degradation dynamics and profound data heterogeneity across varying operational profiles. While convolutional neural networks (CNNs) have shown promise in battery health management, traditional architectures struggle with gradient vanishing in deep feature spaces and lack the adaptive capacity to filter early-cycle noise under diverse degradation conditions. To improve robust RUL estimation across heterogeneous benchmark datasets, this paper proposes a deep learning framework that integrates residual connections with dual-attention mechanisms (ResCNN). Specifically, the residual structures effectively mitigate gradient degradation during the extraction of abstract degradation patterns. Concurrently, a synergistic Squeeze-and-Excitation (SE) and Multi-Head Attention module adaptively calibrates channel-wise feature importance and captures long-range temporal dependencies inherent in complex capacity fade processes. The proposed framework is evaluated under a wide spectrum of degradation conditions and distinct cathode systems (LFP and LCO) using both dataset-specific train/validation/test protocols and strict source-to-target cross-dataset transfer tests. Experimental results demonstrate that ResCNN achieves consistently lower prediction errors than baseline models across the evaluated datasets and maintains positive explanatory power on unseen target datasets without target-domain training. Ablation studies further validate the synergistic contribution of each architectural component toward capturing intrinsic battery aging phenomena. Full article

Microseisms, as the most energetic component of the Earth’s background noise field, represent a forefront area of research where precise location of their sources is paramount. This study systematically investigates the spatiotemporal characteristics of 50 mHz Rayleigh wave microseisms using the dense Shandong array deployed in eastern China, through beamforming and a multi-array combined analysis. The results reveal that the incident direction of the Rayleigh waves exhibits distinct temporal and seasonal variations, primarily originating from four back-azimuth sectors. To further constrain the source regions, we integrate background noise data from the Alaska array and the Venezuela array (supplemented by the Indonesia array). The multi-array product back-projection, by cross-constraining back-azimuths from geographically separated arrays, mitigates the inherent ambiguity of single-array analyses and enables robust global source localization. This approach not only improves the reliability of source attribution but also demonstrates the potential of using microseismic noise as a passive tool for monitoring ocean wave activity and investigating solid-Earth structure. The combined analysis identifies four microseism source regions (M1–M4): the Bering Sea–Gulf of Alaska–Aleutian Islands, the central South Pacific, the southwestern Indian Ocean off southern Africa, and the northeastern North Atlantic–Northern Europe. These source regions fundamentally correspond to areas of elevated significant wave height, confirming the coupled ocean–solid Earth excitation mechanism. These findings provide a methodological basis for future applications of multi-array microseismic monitoring in ocean-climate studies and seismic imaging. Full article

Microseismic monitoring is essential for the early warning of mine dynamic disasters; however, weak signal characteristics and strong environmental noise often lead to missed detections and false alarms. To address these challenges, this study proposes an optimized U-Net model for multi-class microseismic signal recognition under low-signal-to-noise-ratio conditions. The method combines Short-Time Fourier Transform, a U-Net encoder–decoder architecture, residual learning, and squeeze-and-excitation attention modules to enhance weak feature extraction and noise suppression. A multi-source dataset containing microseismic, knocking, blasting, noise, and earthquake signals was constructed using both field-measured data and public seismic datasets. Experimental results show that the proposed model achieved an overall validation accuracy of 99.25% and excellent recall performance for microseismic events. Under extreme noise conditions with a signal-to-noise ratio of −5 dB, the model still maintained a microseismic recognition accuracy of 98.25%. Comparative experiments further demonstrate that the integration of Short-Time Fourier Transform and residual attention modules significantly improves robustness and weak-signal discrimination capability. The proposed method provides an effective approach for intelligent microseismic monitoring and mine dynamic disaster early warning. Full article