Search Results (255)

The modernization of legacy industrial machines is a major challenge in intelligent automation, particularly when critical assets must be upgraded without replacing high-value mechanical and hydraulic infrastructure. This paper presents an industrial case study on the intelligent PLC-based retrofit of an obsolete Neyrpic Digipid speed governor installed in a Kaplan turbine. The proposed solution replaces a closed, vendor-dependent controller with an open Siemens ET 200SP architecture programmed in TIA Portal, integrating existing sensors, hydraulic actuators, redundant speed acquisition, sequential state-machine control, and a digital distributor–runner blade Cam Curve. A key technical contribution is the diagnosis and mitigation of hydraulic hunting in the distributor position loop. The instability was traced to the interaction between integral control action and the intrinsic integrating behavior of the hydraulic actuator, leading to the adoption of a proportional-only position tracking strategy. The system was validated through Factory Acceptance Tests (FATs) and Site Acceptance Tests (SATs), including signal verification, startup, synchronization, load acceptance and emergency load rejection. Quantitative results demonstrate that during initial commissioning of the new PLC-based PI position loop, the LVDT position error reached 41.59% peak-to-peak, with 351.2 servo-valve reversals per minute. Disabling the integral action reduced the peak-to-peak position error to below 1.5% and eliminated steady-state valve reversals under the tested operating conditions. Separately, the historical 0.966 V oscillation detected in the legacy analog-input chain was resolved during the retrofit. During no-load startup, the unit reached 97% of nominal speed in 49.4 s, with a maximum overshoot of 2.3% and a speed tracking standard deviation of 1.1%. The complete operational cycle was successfully validated under real industrial conditions, including a near-nominal load-rejection test (approximately 2.25 MW), during which the measured speed peaked at 126.4% of nominal speed and the shutdown sequence was completed without protection-system malfunction. The results show that open PLC-based retrofits can improve maintainability, diagnostics and operational reliability in safety-critical industrial machines, while establishing a foundation for future SCADA integration and condition-based maintenance. Full article

This study presents and evaluates a system-level optimization of an electrical resistance tomography (ERT) measurement platform based on a ZYNQ-7020 all-programmable system-on-chip (APS-SoC). The design combines deterministic programmable-logic (PL) acquisition, processing-system (PS) configuration and communication scheduling, AXI/DMA data movement, Gigabit Ethernet transmission, and a seventh-order Butterworth excitation filter. FFT-based amplitude extraction and Tikhonov reconstruction remain on the host computer so that the reconstruction algorithm and regularization settings remain identical for the baseline and proposed systems; the present prototype is therefore not claimed as a fully standalone smart sensor. Under the same 16-electrode tap-water testing configuration, the average frame rate increased from 58.23 ± 1.88 FPS to 123.02 ± 1.62 FPS (mean ± sample standard deviation, n = 10), end-to-end latency decreased from 5.2 ms to 2.1 ms, SFDR increased from 68 dB to 95 dB, and SSIM increased from 0.72 to 0.94. In one representative static hardware record at 160 kHz, the calculated amplitude-stability SNR values were 65 dB and 100 dB for the baseline and proposed excitation paths, respectively, while total harmonic distortion decreased from 15.2% to 7.5%. These single-condition signal quality values are descriptive rather than uncertainty-bounded performance specifications. The image-quality differences are attributed primarily to cleaner boundary-voltage measurements with an unchanged reconstruction method, whereas the frame-rate gain reflects the combined PL/PS data path and Gigabit Ethernet upgrade. Full article

Purification systems in water treatment, gas upgrading, bioprocessing, pharmaceuticals, and resource recovery operate under disturbances that expose the limits of static design and end-point analytics. The central question is no longer whether a model can reproduce historical data, but whether the process can infer its internal state early enough to protect purity, recovery, productivity, and robustness. This critical review examines how purification is moving from monitoring toward self-optimizing operation through process analytical technology, operando sensing, chemometrics, soft sensors, digital shadows, digital twins, hybrid models, and control-oriented optimization. Distinct from recent platform-specific or method-specific reviews, it compares membranes, adsorption and cyclic gas separations, chromatography, and integrated purification trains through the operational chain that links measurement, state estimation, model updating, decision support, and closed-loop action. The review also provides a cross-platform maturity framework and minimum reporting expectations for operational claims. The strongest published operational evidence remains concentrated in continuous downstream bioprocessing, while membrane and adsorption systems contribute major advances in fouling inference, adaptive surrogates, and state estimation. The review concludes by defining the evidentiary conditions required for trustworthy self-optimizing purification. Full article

Robotic belt grinding is an effective and widely adopted finishing method for superalloys, offering notable advantages such as high material removal capability, low heat input, and reduced workpiece damage. In addition, robots can readily integrate multiple sensors—such as infrared radiation cameras, force sensors, and high-speed cameras—which facilitate real-time monitoring of the grinding process and thereby enhance grinding quality control. With the establishment and continuous advancement of large-scale artificial intelligence (AI) data models, new breakthroughs have emerged in the optimization of robotic grinding processes. Owing to its dexterous workspace and advantages in high flexibility and cost-effectiveness, robotic belt grinding has become a critical process for the precision forming of complex curved components such as aero-engine blades and blisks. However, factors such as the limited absolute accuracy of industrial robots, time-varying grinding contact states, and significant transient boundary effects make it difficult for the current constant-parameter open-loop machining mode to simultaneously meet the demands for high material removal efficiency and high surface integrity on complex profiles. This paper systematically reviews the technologies for precision control and process optimization of robotic belt grinding aimed at pointwise precise material removal. First, the structural composition of the robotic belt grinding system and the material removal mechanism are analyzed. Then, centered on the compensation concept, a hierarchical progressive technical framework is outlined, covering geometric calibration compensation, force/position hybrid online compensation, transient entry boundary compensation, and system-level comprehensive compensation of multi-source errors, with a comparison of the applicable scenarios and the effects on shape and property control at each level. Furthermore, under the support of effective compensation, the collaborative optimization methods of material removal modeling, multi-objective optimization of process parameters, force-constrained trajectory planning, and intelligent adaptive processes are elaborated. Finally, current technical bottlenecks are summarized, and future trends in next-generation adaptive grinding technology driven by digital twins and embodied intelligence are envisioned. This review aims to provide a systematic theoretical reference for the high-precision and intelligent upgrading of robotic precision grinding systems. Full article

Carrying out quality grading detection on the harvested dragon fruit is an important step in the dragon fruit industry. To reduce the high costs and damage rates caused by this process, an online spectral sensor and a weighing sensor embedded at the end effector of the dragon fruit-picking robot were designed to detect the sugar content, hardness and weight of the dragon fruits in real time during the picking process, thereby achieving the quality classification of the dragon fruits. After collecting the spectral data of dragon fruit, typical linear and nonlinear machine learning methods were used to establish prediction models for SSC-edge, SSC-center and hardness of dragon fruit. The results showed that PLSR models were selected as optimal models for prediction sugar content and hardness, and R² of test set for SSC-edge, SSC-center and hardness are 0.876, 0.826 and 0.902, respectively. Subsequently, the dragon fruits were classified based on the weighing sensor, and the SSC-center and hardness were predicted. The results showed that the established quality prediction model and the prototype could achieve the integrated operation of non-destructive quality detection and grading of dragon fruit during picking. The study provides technical support for the intelligent upgrade of fruit-harvesting equipment and the grading operations. Full article

Securing IoT networks presents fundamental challenges rooted in hardware constraints: firmware is often non-upgradeable and every security boundary is fixed at manufacture. Machine learning-based intrusion detection offers a scalable response, yet nearly all published systems assume clean training data and clean inference conditions. Production IoT environments satisfy neither assumption. Sensors degrade, packets drop, and adversaries deliberately corrupt telemetry streams to evade detection. The framework described here is built around that reality. The proposed framework is distinguished from prior work by four design decisions. First, three encoding branches, a residual DNN, a 1D-CNN, and a BiLSTM, are run in parallel and are fused by concatenation, each capturing structural patterns in tabular traffic data that the others miss. Second, a dual-view consistency loss trains the model under simultaneous feature masking and Gaussian noise, penalizing prediction divergence between two independently corrupted views of the same sample. Third, we introduce entropy-weighted attention: rather than fixed learned weights, per-feature importance is adjusted dynamically from information entropy measured across training batches, giving higher-entropy features stronger influence because they carry more discriminative variation. Fourth, branch-dropout regularization randomly silences entire branches during training, forcing each to develop independently useful representations instead of co-adapting. Class imbalance is handled through severity-aware loss weighting which scales contributions by the operational cost of missing each attack category, not purely by inverse frequency. On UNSW-NB15, the full model achieves 99.99% accuracy, 100% precision, 99.97% recall, and a false-negative rate of 2.65 × 10⁻⁴—the lowest across all compared architectures. Full article

With the development of industrial intelligence, traditional material handling systems suffer from insufficient flexibility, low functional integration, and weak fire safety response. To solve these problems, this paper designs an Arduino-based multifunctional intelligent material handling crane system with integrated fire safety protection. The system adopts a modular multi-sensor fusion architecture, realizing environmental perception, automatic path planning, and dual fire safety protection (smoke alarm + automatic fire extinguishing). Experiments were carried out in a laboratory-controlled environment with the system in as the benchmark; the results show that the operation efficiency of object handling is improved by 29.6%. This prototype system provides an experimental reference for the intelligent and safe upgrading of small and medium-sized warehousing material handling equipment. All experiments were completed in a controlled laboratory environment. Full article

Precise time synchronization is foundational for future intelligent transportation systems (ITS), where safety-critical functions like cooperative Vehicle-to-Everything (V2X) communication and multi-sensor fusion demand a leap from sub-microsecond- to nanosecond-level precision. Standard protocols like the Precision Time Protocol (PTP) are limited by inherent errors (e.g., timestamping inaccuracies and clock drift) that are typically only solvable with expensive hardware upgrades. This paper proposes a cost-effective, software-based solution. We introduce a novel method that leverages deep reinforcement learning (DRL) to actively predict and compensate for these synchronization errors in real time. An experimental environment is constructed to rigorously evaluate the performance of the proposed method. The results demonstrate that our approach achieves a significant leap in synchronization accuracy, showcasing its potential to meet the stringent timing demands of future intelligent transportation. Full article

This paper proposes a system-wide fault diagnosis method for wireless power transfer (WPT) systems. This method enables the comprehensive fault diagnosis of key components in WPT systems by using only a single current sensor. It requires no controller upgrades, offering a cost-effective and minimally invasive solution. The fault diagnosis method is based on a support vector machine (SVM) algorithm; the Hierarchy-SVM algorithm is proposed which reduces training time to 54% and recognition time to 16.5% of those required by traditional multi-class SVM algorithms, while maintaining comparable accuracy, which was tested under the same dataset and hardware configuration. Lastly, experimental verification is conducted. The experimental results demonstrate that the proposed method achieves a more than 95% accuracy rate in identifying various faults, with an average single identification time of average 14.19 ms. Full article

In structural health monitoring, purely data-driven methods for deformation prediction are often susceptible to time-varying boundary conditions under complex operating scenarios, leading to insufficient physical interpretability and limited generalization across different conditions. To address these challenges, this study proposes a Physics-Informed Dual-branch Long Short-Term Memory framework (PINN-DualSHM). The framework employs dual-branch LSTMs to separately extract temporal features of structural mechanical responses and environmental thermal effects. Dynamic decoupling and fusion of these heterogeneous features are achieved through an adaptive cross-attention mechanism. Furthermore, physical priors, including the thermodynamic superposition principle and structural settlement monotonicity, are embedded into the loss function as regularization terms, complemented by a dual uncertainty quantification system based on heteroscedastic regression and MC Dropout. Experimental results based on long-term measured data from an industrial base project in Shenzhen demonstrate that PINN-DualSHM significantly outperforms baseline models such as LSTM, CNN-LSTM, and GAT-LSTM. Specifically, the Root Mean Square Error (RMSE) is reduced by 65.25%, and the coefficient of determination (R²) reaches 0.925. Physical consistency analysis confirms that the introduction of physical constraints effectively suppresses anomalous predictive fluctuations that violate mechanical laws. Uncertainty decomposition reveals that aleatoric uncertainty is dominant (93.7%), objectively indicating that the current system’s accuracy bottleneck lies in sensor noise rather than model capability. By enhancing prediction accuracy while providing credible quantitative assessments and physical interpretability, the proposed method provides a scientific basis for the operation, maintenance optimization, and upgrading decisions of SHM systems. Full article

The construction industry faces persistent challenges in productivity, efficiency, and sustainability. Digital twin (DT) technology has emerged as a promising pathway for lifecycle optimisation, yet its construction adoption remains limited. Key barriers include fragmentation across project phases, weak data continuity at handover, and conceptual ambiguity between DT and Building Information Modelling (BIM). This systematic literature review analyses 160 peer-reviewed studies (2018–2026) selected from 463 Scopus records using a PRISMA-guided process and inter-rater reliability testing (Cohen’s κ = 0.83). The review clarifies that DTs extend beyond BIM in three ways: they enable bidirectional, automated physical-digital data exchange; integrate heterogeneous real-time sources such as IoT sensors and operational systems; and maintain lifecycle continuity from design through to end-of-life. Select advanced implementations report notable performance gains. These include rework and logistics reductions of up to 80%, cost savings of approximately 5%, schedule acceleration of around two months, energy reductions of 15–30%, and maintenance cost reductions of 10–25%. These figures reflect case-level outcomes from high-performing pilots and should not be read as typical industry benchmarks. Broader adoption remains constrained by interoperability gaps, data quality challenges, digital maturity deficits, misaligned stakeholder incentives, and paper-based regulatory environments. DTs represent a socio-technical transformation, not a standalone technology upgrade. Realising their potential requires coordinated progress in standards development, governance frameworks, collaborative delivery models, and workforce capability. Future research should focus on scalable interoperability, longitudinal lifecycle value validation, human-centred adoption strategies, and sustainability assessment methods to support evidence-based diffusion of DTs in the built environment. Full article

To address the technical challenge where traditional high-pressure, large-flow emulsion pump stations cannot adapt to the drastic flow rate changes in hydraulic supports due to the fixed displacement of their quantitative pumps—leading to frequent system unloading, severe impacts, and damage—this study proposes an intelligent flow control method based on the digital flow distribution principle for actively perceiving and matching support demands. Building on this method, a compact, electro-hydraulically separated prototype with stepless flow regulation was developed. The system integrates high-speed switching solenoid valves, a piston push rod, a plunger pump, sensors, and a controller. By monitoring piston position in real time, the controller employs an optimized combined regulation strategy that integrates adjustable duty cycles across single, dual, and multiple cycles. This dynamically adjusts the switching timing of the pilot solenoid valve, thereby precisely controlling the closure of the inlet valve. As a result, part of the fluid can return to the suction line during the compression phase, fundamentally achieving accurate and smooth matching between the pump output flow and support demand, while significantly reducing system fluctuations and impacts. This research adopts a combined approach of co-simulation and experimental validation to deeply investigate the dynamic coupling relationship between the piston’s extreme position and delayed valve closure. It further establishes a comprehensive dynamic coupling model covering the response of the pilot valve, actuator motion, and backflow control characteristics. By analyzing key parameters such as reset spring stiffness, piston cylinder diameter, and actuator load, the system reliability is optimized. Evaluation of the backflow strategy and delay phase verifies the effectiveness of the multi-mode composite regulation strategy based on digital displacement pump technology, which extends the effective flow range of the pump to 20–100% of its rated flow. Experimental results show that the system achieves a flow regulation range of 83% under load and 57% without load, with energy efficiency improved by 15–20% due to a significant reduction in overflow losses. Compared with traditional unloading methods, this approach demonstrates markedly higher control precision and stability, with substantial reductions in both flow root mean square error (53.4 L/min vs. 357.2 L/min) and fluctuation amplitude (±3.5 L/min vs. ±12.8 L/min). The system can intelligently respond to support conditions, providing high pressure with small flow during the lowering stage and low pressure with large flow during the lifting stage, effectively achieving on-demand and precise supply of dynamic flow and pressure. The proposed “demand feedforward–flow coordination” control architecture, the innovative electro-hydraulically separated structure, and the multi-cycle optimized regulation strategy collectively provide a practical and feasible solution for upgrading the fluid supply system in fully mechanized mining faces toward fast response, high energy efficiency, and intelligent operation. Full article

Female soccer is rapidly becoming a widely practiced sport at different levels: this opens up a new demand for systems meant to protect athletes from head impacts or to monitor their effects. The market is offering some solutions in similar sports, but the specificity and high relevance of soccer encourage the development of a dedicated solution. From market analysis, technology scouting, and ethnographic research a set of functional and technical requirements have been defined and proposed. The designed instrumented head band is equipped with one Inertial Measurement Unit (IMU) in the occipital area and four contact pressure sensors on the sides. The concept design is low-cost and open-architecture, prioritizing accessibility over complexity. The modularity also ensures that each component (sensing, battery, communication) can be replaced or upgraded independently, enabling iterative refinement and integration into future sports safety systems. In addition to safety monitoring for injury prevention or detection of the traumatic impact, the system is relevant for supporting performance monitoring, rehabilitation or post-injury recovery and other important applications. System engineering has started and the next step is building the prototypes for testing and validation. Full article

This paper presents a low-cost, modular ankle–foot prosthesis that integrates an S-shaped compliant foot with a parallel spring–short-stroke actuator branch to balance energy return, impact attenuation, and rapid personalization. The design follows an FDM-oriented CAD/CAE workflow using PETG and interchangeable modules (foot, ankle unit, pylon adapter). Finite-element analyses of heel-strike, mid-stance, and toe-off load cases, supported by bench checks, show strain localization in intended flexural regions, a minimum safety factor of 15 for the housing, and peak-stress reduction after geometric refinements (increased transition radii and local ribs). The modular layout simplifies servicing and allows quick tuning of stiffness and damping without redesigning the load-bearing structure. The results indicate an engineeringly realistic path toward accessible prosthetics and provide a basis for subsequent upgrades toward semi-active control and sensor-assisted damping. Full article

This paper proposes a comprehensive framework for integrating Digital Twins (DT) with real-time traffic optimization systems to enhance urban mobility management in Smart Cities. Using the Pobitno Roundabout in Rzeszów as a case study, we established a calibrated microsimulation model (validated via the GEH statistic) that serves as the core of the proposed Digital Twin. The study goes beyond static scenario analysis by introducing an Adaptive Inflow Metering (AIM) logic designed to interact with IoT sensor data. While traditional geometrical upgrades (e.g., turbo-roundabouts) were analyzed, simulation results revealed that geometrical changes alone—without dynamic control—may fail under peak load conditions (resulting in LOS F). Consequently, the research demonstrates how the DT framework allows for the testing of “Software-in-the-Loop” (SiL) solutions where Python-based algorithms dynamically adjust inflow parameters to prevent gridlock. The findings confirm that combining physical infrastructure changes with digital, real-time optimization algorithms is essential for achieving sustainable “green transport” goals and reducing emissions in congested urban nodes. Full article