Search Results (150)

This paper proposes a deep reinforcement learning-based predictive control scheme to address cushion pressure prediction and stabilization in hovercraft systems subject to modeling complexity, dynamic instability, and system delay. Notably, this work introduces a long short-term memory (LSTM) network with a temporal sliding window specifically designed for hovercraft cushion pressure forecasting. The model accurately captures the dynamic coupling between fan speed and chamber pressure while explicitly incorporating inherent control lag during airflow transmission. Furthermore, a novel adaptive behavior cloning mechanism is embedded into the twin delayed deep deterministic policy gradient with behavior cloning (TD3-BC) framework, which dynamically balances reinforcement learning (RL) objectives and historical policy constraints through an auto-adjusted weighting coefficient. This design effectively mitigates distribution shift and policy degradation in offline reinforcement learning, ensuring both training stability and performance beyond the behavior policy. By integrating the LSTM prediction model with the adaptive TD3-BC algorithm, a fully data-driven control architecture is established. Finally, simulation results demonstrate that the proposed method achieves high accuracy in cushion pressure tracking, significantly improves motion stability, and extends the operational lifespan of lift fans by reducing rotational speed fluctuations. Full article

The interruption of real-time state space representation (SSR) corrections significantly degrades the performance of precise point positioning (PPP). To address this challenge, we propose a novel residual-enhanced iTransformer model specifically designed for BeiDou navigation satellite system (BDS) SSR prediction. Unlike conventional approaches including polynomial fitting, harmonic modeling, and autoregressive moving average (ARMA) methods, our framework innovatively integrates residual networks with the iTransformer architecture to effectively capture the complex nonlinear characteristics and non-stationary patterns in satellite clock offsets. The model demonstrates remarkable performance improvements, achieving 72–85% reduction in prediction error compared with traditional ARMA models. Experimental results show that, within 2 h prediction windows, orbit corrections achieve better than 0.1 m (radial), 0.2 m (along-track), and 0.2 m (cross-track) accuracy, while clock corrections maintain sub-0.5 ns precision. Most importantly, during 30 min SSR outages, BDS real-time PPP utilizing our predicted corrections sustains positioning accuracy within 10 cm in all east, north, and up directions, representing over 80% improvement compared with traditional time-differenced carrier phase (TDCP) methods. This work establishes an effective solution for maintaining high-precision positioning services during SSR interruptions. Full article

Power transformers are critical assets in modern power grids, where failures can lead to significant operational disruptions and financial losses. Dissolved Gas Analysis (DGA) is a key sensor-based technique widely used for condition monitoring, but traditional diagnostic approaches rely on deterministic thresholds that overlook uncertainty in degradation dynamics. This paper proposes a probabilistic framework for Prognostics and Health Management (PHM) of power transformers, integrating self-adaptive Auto Regressive Integrated Moving Average modeling with a probabilistic reformulation of Duval’s graphical methods. The framework enables automated estimation of fault types and failure likelihood directly from DGA sensor data, without requiring labeled datasets or expert-defined rules. Dissolved gas dynamics are forecasted using time-series models with residual-based uncertainty quantification, allowing probabilistic fault inference from predicted gas trends without assuming deterministic persistence of a specific fault type. A sequential pipeline is developed for real-time fault tracking and reliability assessment, aligned with IEC, IEEE, and CIGRE standards. Two case studies validate the method: one involving gas loss in an experimental setup and another examining thermal degradation in a 345 kV transformer. Results show that the framework improves diagnostic reliability, supports early fault detection, and enhances predictive maintenance strategies. By combining probabilistic modeling, time-series forecasting, and sensor-based diagnostic inference, this work contributes a practical and interpretable PHM solution for sensor-enabled monitoring environments in modern power grids. Full article

In battery energy storage systems, accurately estimating battery capacity and state of health is crucial to ensure satisfactory operation and system efficiency and reliability. However, these tasks present particular challenges under irregular charge–discharge conditions, such as those encountered in renewable energy integration and electric vehicles, where heterogeneous cycling accelerates degradation. This study introduces a hybrid deep learning framework to address these challenges. It combines convolutional layers for localized feature extraction, bidirectional recurrent units for sequential learning and a temporal attention mechanism. The proposed hybrid deep learning model, termed CBATE-Net, uses ensemble averaging to improve stability and emphasizes degradation-critical intervals. The framework was evaluated using voltage, current and temperature signals from four benchmark lithium-ion cells across complete life cycles, as part of the NASA dataset. The results demonstrate that the proposed method can accurately track both smooth and abrupt capacity fade while maintaining stability near the end of the life cycle, an area in which conventional models often struggle. Integrating feature learning, temporal modelling and robustness enhancements in a unified design provides the framework with the ability to make accurate and interpretable predictions, making it suitable for deployment in real-world battery energy storage applications. Full article

The trajectory-tracking capability of autonomous mining trucks is critical for accomplishing transportation tasks efficiently. However, due to the diverse road surfaces and rugged terrains in open-pit mines, the existing vehicle dynamics models struggle to accurately capture the complex tire–ground interactions. As a result, conventional trajectory-tracking control methods that rely on linear vehicle dynamics models suffer from degraded tracking performance. To this end, this paper proposes a novel trajectory-tracking control framework that integrates model predictive control (MPC) with model-free adaptive control (MFAC). A warm-start strategy is employed to improve the computational efficiency of MPC, while MFAC is utilized to provide real-time compensation for the control deviations generated by MPC during the trajectory-tracking process. To validate the effectiveness of the proposed trajectory-tracking control method, co-simulations were conducted on the CarSim and MATLAB/Simulink platforms under various road conditions and driving scenarios. Simulation results demonstrate that the proposed method can effectively enhance the trajectory-tracking performance of autonomous mining trucks. For instance, under the S-condition with Class E road elevation, the proposed method achieves improvements of approximately 90.83%, 15.05%, and 71.93% in the mean error, maximum error, and root mean square error (RMSE), respectively, compared with the conventional LQR-based trajectory-tracking control method. In addition, the computation time of MPC is only 2 ms, which significantly improves the overall performance of the trajectory-tracking controller. Full article

Turnouts are critical components of railway infrastructure, ensuring operational flexibility but also representing a significant share of track maintenance costs. The frog, as the most vulnerable part of a turnout, is subject to severe wear and degradation, requiring frequent inspection and maintenance. Traditional manual inspection methods are costly, labour-intensive, and susceptible to subjectivity. This study explores a data-driven approach to condition monitoring of fixed-turnout frogs using standard track recording car measurements. By leveraging over 20 years of longitudinal level and rail surface signal data from the Austrian track-recording measurement car, we assess the feasibility of using existing measurement data for predictive maintenance. Six complementary approaches are proposed to evaluate frog condition, including track geometry assessment, ballast condition analysis, rail surface irregularity detection, and axle box acceleration-based monitoring. Results indicate that data-driven monitoring enhances maintenance decision-making by identifying deterioration trends, reducing reliance on manual inspections, and enabling predictive interventions. The integration of standardised measurement data with advanced analytical models offers a cost-effective and scalable solution for turnout maintenance. Full article

Atmospheric corrosion in sand dust environments is driven by deposits that bear chloride, which sustain thin electrolyte layers on metal surfaces. We established a laboratory protocol to replicate this by extracting, formulating, and depositing a preliminary layer of mixed salts from natural dust onto samples, with humidity precisely set using the salt’s deliquescence behavior. Degradation was tracked with SEM/EDS, 3D profilometry, XRD, and electrochemical analysis. Bare steel showed progressive yet decelerating attack as rust evolved from discrete islands to a lamellar network; while this densification limited transport, its internal cracks and interfacial gaps trapped chlorides, sustaining activity beneath the rust. In contrast, QPQ-treated steel remained largely protected, with damage localized at coating defects as raised rust nodules, while intact regions maintained low electrochemical activity. By coupling salt chemistries derived from the field with humidity control guided by deliquescence and diagnostics across multiple scales, this study provides a reproducible laboratory pathway to predict atmospheric corrosion. Full article

Exoskeleton-assisted bodyweight support training (BWST) has demonstrated enhanced neurorehabilitation outcomes in which joint motion prediction serves as the critical foundation for adaptive human–machine interactive control. However, joint angle prediction under dynamic unloading conditions remains unexplored. This study introduces an adaptive wavelet-denoising fusion (AWDF) model to predict lower-limb joint angles during BWST. Utilizing a custom human-tracking bodyweight support system, time series data of surface electromyography (sEMG), and inertial measurement unit (IMU) from ten adults were collected across graded bodyweight support levels (BWSLs) ranging from 0% to 40%. Systematic comparative experiments evaluated joint angle prediction performance among five models: the sEMG-based model, kinematic fusion model, wavelet-enhanced fusion model, late fusion model, and the proposed AWDF model, tested across prediction time horizons of 30–150 ms and BWSL gradients. Experimental results demonstrate that increasing BWSLs prolonged gait cycle duration and modified muscle activation patterns, with a concomitant decrease in the fractal dimension of sEMG signals. Extended prediction time degraded joint angle estimation accuracy, with 90 ms identified as the optimal tradeoff between system latency and prediction advancement. Crucially, this study reveals an enhancement in prediction performance with increased BWSLs. The proposed AWDF model demonstrated robust cross-condition adaptability for hip and knee angle prediction, achieving average root mean square errors (RMSE) of 1.468° and 2.626°, Pearson correlation coefficients (CC) of 0.983 and 0.973, and adjusted R² values of 0.992 and 0.986, respectively. This work establishes the first computational framework for BWSL-adaptive joint prediction, advancing human–machine interaction in exoskeleton-assisted neurorehabilitation. Full article

During deep coalbed methane (CBM) drilling, wellbore stability is significantly influenced by the interaction between drilling fluid and coal rock. However, quantitative data on mechanical degradation under long-term high-temperature and high-pressure conditions are lacking. This study subjected coal cores to immersion in field-formula drilling fluid at 60 °C and 10.5 MPa for 0–30 days, followed by uniaxial and triaxial compression tests under confining pressures of 0/5/10/20 MPa. The fracture evolution was tracked using micro-indentation (µ-indentation), nuclear magnetic resonance (NMR), and scanning electron microscopy (SEM), establishing a relationship between water absorption and strength. The results indicate a sharp decline in mechanical parameters within the first 5 days, after which they stabilized. Uniaxial compressive strength decreased from 36.85 MPa to 22.0 MPa (−40%), elastic modulus from 1.93 GPa to 1.07 GPa (−44%), cohesion from 14.5 MPa to 5.9 MPa (−59%), and internal friction angle from 24.9° to 19.8° (−20%). Even under 20 MPa confining pressure after 30 days, the strength loss reached 43%. Water absorption increased from 6.1% to 7.9%, showing a linear negative correlation with strength, with the slope increasing from −171 MPa/% (no confining pressure) to −808 MPa/% (20 MPa confining pressure). The matrix elastic modulus remained stable at 3.5–3.9 GPa, and mineral composition remained unchanged, confirming that the degradation was due to hydraulic wedging and lubrication of fractures rather than matrix damage. These quantitative thresholds provide direct evidence for predicting wellbore stability in deep CBM drilling. Full article

Autonomous robotic manipulators in industrial environments face significant challenges, including time-varying payloads, multi-source disturbances, and real-time computational constraints. Traditional model predictive control frameworks degrade by over 40% under model uncertainties, while conventional adaptive techniques exhibit convergence times incompatible with industrial cycles. This work presents a hybrid adaptive model predictive control framework integrating edge artificial intelligence with dual-stage parameter estimation for 6-DoF industrial manipulators. The approach combines recursive least squares with a resource-optimized neural network (three layers, 32 neurons, <500 KB memory) designed for industrial edge deployment. The system employs innovation-based adaptive forgetting factors, providing exponential convergence with mathematically proven Lyapunov-based stability guarantees. Simulation validation using the Fanuc CR-7iA/L manipulator demonstrates superior performance across demanding scenarios, including precision laser cutting and obstacle avoidance. Results show 52% trajectory tracking RMSE reduction (0.022 m to 0.012 m) under 20% payload variations compared to standard MPC, while achieving sub-5 ms edge inference latency with 99.2% reliability. The hybrid estimator achieves 65% faster parameter convergence than classical RLS, with 18% energy efficiency improvement. Statistical significance is confirmed through ANOVA (F = 24.7, p < 0.001) with large effect sizes (Cohen’s d > 1.2). This performance surpasses recent adaptive control methods while maintaining proven stability guarantees. Hardware validation under realistic industrial conditions remains necessary to confirm practical applicability. Full article

A physics-based vehicle–track coupled dynamic model embedding a hydraulic electromechanical regenerative damper (HERD) is developed to quantify electrical power recovery and wear depth in high-speed service. The HERD subsystem resolves compressible hydraulics, hydraulic rectification, line losses, a hydraulic motor with a permanent-magnet generator, an accumulator, and a controllable; co-simulation links SIMPACK with MATLAB/Simulink. Wheel–rail contact is computed with Hertz theory and FASTSIM, and wear depth is advanced with the Archard law using a pressure–velocity coefficient map. Both HERD power regeneration and wear depth predictions have been validated against independent measurements of regenerated power and wear degradation in previous studies. Parametric studies over speed, curve radius, mileage and braking show that increasing speed raises input and output power while recovery efficiency remains 49–50%, with instantaneous electrical peaks up to 425 W and weak sensitivity to curvature and mileage. Under braking from 350 to 150 km/h, force transients are bounded and do not change the lateral wear pattern. Installing HERD lowers peak wear in the wheel tread region; combining HERD with flexible wheelsets further reduces wear depth and slows down degradation relative to rigid wheelsets and matches measured wear more closely. The HERD electrical load provides a physically grounded tuning parameter that sets hydraulic back pressure and effective damping, which improves model accuracy and supports calibration and updating of digital twins for maintenance planning. Full article

The transformation of smectite to illite documents multi-scale water–rock–hydrocarbon interaction dynamics. Current studies predominantly emphasize the influence of inorganic systems on this process, while overlooking the dynamic regulation by organic matter and the synergistic effects of multiple controlling factors under actual geological conditions. In this study, we conducted integrated semi-open pyrolysis experiments on natural samples from the Chang-7 Member and hydrothermal experiments using synthetic analogs. The illitization process of smectite was characterized through XRD analysis and SEM observations, while organic geochemical testing was employed to track the corresponding thermal evolution of organic matter. The semi-open pyrolysis results reveal that significant changes in illite–smectite (I/S) mixed layer minerals and illite content/morphology occur above 320 °C, which coincides with the critical threshold for extensive organic matter evolution. Thermal degradation of organic matter generates pore space, thereby enhancing water–rock interactions involving clay minerals. This demonstrates the co-evolution of organic matter and smectite, and indicates that temperature indirectly influences illitization by regulating organic matter thermal evolution. The hydrothermal simulation experiments demonstrate the early-stage characteristics of illitization. Unlike long-term geological evolution, K⁺ under experimental conditions primarily originates from the aqueous medium due to kinetic constraints on feldspar dissolution. Notably, organic matter regulates K⁺ partitioning dynamics—increased organic matter content hinders K⁺ incorporation into smectite interlayers, thereby suppressing the illitization process. Cross-system experimental analysis reveals that organic matter exhibits temporally dependent dual functionality, serving both mediating and modulating roles within inorganic diagenetic systems. This study delineates diagnostic-stage-dependent mechanisms governing smectite illitization through multifactorial synergistic interplay, establishing a predictive framework applicable to organic-rich systems exemplified by the Chang-7 Shale. Full article

This work provides the first demonstration of FFTCCV as a dual-purpose method, serving both as a real-time diagnostic tool and as a phase- and morphology-engineering strategy. By adjusting the scan rate, FFTCCV directs the crystallographic evolution of Ni (OH)₂ on Ni foam—stabilizing α-nanoflakes at 0.7 V·s⁻¹ and β-platelets at 0.007 V·s⁻¹—while simultaneously enabling electrode-resolved ΔQ tracking and predictive state-of-health (SoH) monitoring. This approach enabled the precise regulation of electrode morphology and phase composition, yielding high areal capacitance (546.5 mF·cm⁻² at 5 mA·cm⁻²) with ~75% retention after 3000 cycles. These improvements advance the development of high-performance micro-supercapacitors, facilitating their integration into wearable and miniaturized devices where compact and durable energy storage is required. Beyond performance enhancement, FFTCCV also enabled continuous monitoring of capacitance during extended operation (up to 40,000 s). By recording both anodic and cathodic responses, the method provided time-resolved insights into device stability and revealed characteristic signatures of electrode degradation, phase transitions, and morphological changes. Such detection allows recognition of early failure pathways that are not accessible through conventional testing. This monitoring capability functions as an embedded health sensor, offering a pathway for predictive diagnosis of supercapacitor failure. Such functionality is particularly important for energy-driven actuators and smart materials, where uninterrupted operation and preventive maintenance are critical. FFTCCV therefore provides a scalable strategy for developing energy-autonomous microsystems with improved performance and real-time state-of-health monitoring. Full article

Predictive maintenance systems increasingly leverage diverse sensor modalities to improve failure prognostics and remaining useful life (RUL) estimation. However, integrating heterogeneous data types—vibration, temperature, acoustic, and visual sensors—typically requires complex fusion architectures. This paper proposes a unified linear classification–regression framework that addresses predictive maintenance through a shared measurement space approach. The developed method employs Linear Discriminant Analysis to establish hyperplane boundaries partitioning the measurement space into nominal, warning, and failure regions. By tracking each data point’s signed distance to these learned boundaries, this research generates continuous RUL predictions through linear regression mapping. The framework’s key innovation lies in seamlessly integrating heterogeneous sensor modalities without requiring separate preprocessing pipelines or complex fusion layers—each modality contributes to fault detection and RUL estimation based on its discriminative power in the joint feature space. While linear assumptions may simplify complex non-linear failure patterns, the proposed approach offers significant advantages in interpretability, computational efficiency, and deployment ease. Validation on the C-MAPSS turbofan engine degradation dataset demonstrates that while not achieving state-of-the-art performance, the framework provides a practical foundation accommodating data-driven, physics-based, and knowledge-based modeling paradigms within a unified architecture, making it valuable for industrial applications requiring transparent multi-modal integration. Full article

Centrifugal irrigation pumps in Southern Xinjiang face severe performance degradation due to high fine-sediment loads in canal water. This study combines Eulerian multiphase simulations with experimental validation to investigate the coupled effects of sediment size (0.05~0.8 mm) and concentration (5~20%) on hydraulic performance. Numerical models incorporating Realizable k–ε turbulence closure and discrete phase tracking reveal two critical thresholds: (1) particle sizes ≥ 0.4 mm trigger a phase transition from localized disturbance to global flow disorder, expanding low-pressure zones by 37% at equivalent concentrations; (2) concentrations exceeding 13% accelerate nonlinear pressure decay through collective particle interactions. Velocity field analysis demonstrates size-dependent attenuation mechanisms: fine sediments (≤0.2 mm) cause gradual dissipation via micro-scale drag, while coarse sediments (≥0.6 mm) induce “cliff-like” velocity drops through inertial impact-blockade chains. Experimental wear tests confirm simulation accuracy in predicting erosion hotspots at impeller inlets/outlets. The identified synergistic thresholds provide critical guidelines for anti-wear design in sediment-laden irrigation systems. Full article