Search Results (505)

Angle crack defects significantly affect compressor blade radial deformation characteristics, posing critical challenges for reliability assessment under operational uncertainties. This study proposes a novel osprey optimization algorithm (OOA)-optimized Kriging and radial basis function (RBF) method (OOA-KR) for the efficient reliability evaluation of blade radial clearance with angle crack defects. The approach integrates Kriging’s uncertainty quantification capabilities with RBF neural networks’ nonlinear mapping strengths through an adaptive weighting scheme optimized by OOA. Multiple uncertainty sources including crack geometry, operational temperature, and loading conditions are systematically considered. A comprehensive finite element model incorporating crack size variations and multi-physics coupling effects generates training data for surrogate model construction. Comparative studies demonstrate superior prediction accuracy with RMSE = 0.568 and R² = 0.8842, significantly outperforming conventional methods while maintaining computational efficiency. Reliability assessment achieves 97.6% precision through Monte Carlo simulation. Sensitivity analysis reveals rotational speed as the most influential factor (S = 0.42), followed by temperature and loading parameters. The proposed OOA-KR method provides an effective tool for blade design optimization and reliability-based maintenance strategies. Full article

This paper proposes a flow battery-lithium-ion battery hybrid energy storage system (HESS) bi-level optimization planning method to address flexibility supply-demand balance challenges in regional power grids with high renewable penetration at 220 kV and above voltage levels. The method establishes a planning-operation coordination framework: Upper-level planning minimizes total lifecycle investment and operation-maintenance costs; Lower-level operation incorporates multiple constraints including flexibility gap penalties, voltage fluctuations, and line losses, overcoming single-timescale limitations. The approach enhances global search capability through the Improved Weighted Average Algorithm (IWAA) and optimizes power allocation accuracy using adaptive Variational Mode Decomposition (VMD). Validation using grid data from Southwest China demonstrates significant improvements across five comparative schemes. Results show substantial reductions in total investment costs, penalty costs, voltage fluctuations, and line losses compared to benchmark solutions, enhancing grid power supply stability and verifying the effectiveness of the model and algorithm. Full article

This study aims to evaluate and compare the carbon emissions and reduction strategies of two different slope construction methods—concrete slope protection and ecological sprayed-soil slope protection—using a life-cycle assessment (LCA) approach. The research focuses on identifying key carbon emission sources throughout each stage of the construction, from material production to transportation, construction, and maintenance, with a particular emphasis on the ecological benefits of vegetation in reducing carbon footprints. Results indicate that the ecological slope protection scheme significantly outperforms the concrete scheme, reducing total carbon emissions by 667.21 tons. Furthermore, the ecological solution, due to its carbon sequestration capabilities, is projected to achieve carbon neutrality within 3.66 years after completion, offering a net carbon sequestration benefit of 2422.97 tons over its lifecycle. Optimization strategies across various stages—material production, transportation, construction, and maintenance—further reduce emissions by 56.8%, underscoring the potential for ecological slope protection to contribute to sustainable construction practices. This study not only provides valuable insights into low-carbon construction methods but also highlights the importance of integrating ecological and engineering technologies to meet global carbon reduction goals. Full article

This paper proposes a hybrid predictive maintenance framework that combines the discriminative power of XGBoost with the interpretability of a Bayesian Network automatically learned from sensor data. Targeted at textile manufacturing equipment operating under Industry 4.0 conditions, the system addresses the trade-off between early fault detection and decision transparency. Sensor data, including vibration, temperature, and electric current, were collected from a multi-needle quilting machine using a custom IoT-based platform. A degradation-aware labeling scheme was implemented using historical maintenance logs to assign semantic labels to sensor readings. A Bayesian Network structure was learned from this data via a Hill Climbing algorithm optimized with the Bayesian Information Criterion, capturing interpretable causal dependencies. In parallel, an XGBoost model was trained to improve classification accuracy for incipient faults. Experimental results demonstrate that XGBoost achieved an F1-score of 0.967 on the high-degradation class, outperforming the Bayesian model in raw accuracy. However, the Bayesian Network provided transparent probabilistic reasoning and root cause explanation capabilities—essential for operator trust and human-in-the-loop diagnostics. The integration of both models yields a robust and interpretable solution for predictive maintenance, enabling early alerts, visual diagnostics, and scalable deployment. The proposed architecture is validated in a real production line and demonstrates the practical value of hybrid AI systems in bridging performance and interpretability for predictive maintenance in Industry 4.0 environments. Full article

In recent years, the synergistic effect among production, maintenance, and quality control within manufacturing systems has garnered increasing attention in academic and industrial circles. In high-quality production settings, the real-time identification of minute process deviations holds significant importance for ensuring product quality. Traditional approaches, such as routine quality inspections or Shewhart control charts, exhibit limitations in sensitivity and response speed, rendering them inadequate for meeting the stringent requirements of high-precision quality control. To address this issue, this paper presents an integrated framework that seamlessly integrates stochastic process modeling, dynamic optimization, and quality monitoring. In the realm of quality monitoring, an exponentially weighted moving average (EWMA) control chart is employed to monitor the production process. The statistic derived from this chart forms a Markov process, enabling it to more acutely detect minor shifts in the process mean. Regarding maintenance strategies, a state-dependent preventive maintenance (PM) and corrective maintenance (CM) mechanism is introduced. Specifically, preventive maintenance is initiated when the system is in a statistically controlled state and the inventory level falls below a predefined threshold. Conversely, corrective maintenance is triggered when the EWMA control chart generates an out-of-control (OOC) signal. To facilitate continuous production during maintenance activities, an inventory buffer mechanism is incorporated into the model. Building upon this foundation, a joint optimization model is formulated, with system states, including equipment degradation state, inventory level, and quality state, serving as decision variables and the minimization of the expected total cost (ETC) per unit time as the objective. This problem is formalized as a constrained dynamic optimization problem and is solved using the genetic algorithm (GA). Finally, through a case study of the production process of vibroseis equipment, the superiority of the proposed model in terms of cost savings and system performance enhancement is empirically verified. Full article

Locally Repairable Codes (LRCs) have become the dominant design in wide-stripe erasure coding storage systems due to their excellent locality and low repair bandwidth. In such systems, the repair degree—defined as the number of helper nodes contacted during data recovery—is a key performance metric. However, as stripe width increases, the probability of multiple simultaneous node failures grows, which significantly raises the repair degree in traditional LRCs. Addressing this challenge, we propose a new family of codes called TFR-LRCs (Locally Repairable Codes for balancing fault tolerance and repair efficiency). TFR-LRCs introduce flexible design choices that allow trade-offs between fault tolerance and repair degree: they can reduce the repair degree by slightly increasing storage overhead, or enhance fault tolerance by tolerating a slightly higher repair degree. We design a matrix-based construction to generate TFR-LRCs and evaluate their performance through extensive simulations. The results show that, under multiple failure scenarios, TFR-LRC reduces the repair degree by up to 35% compared with conventional LRCs, while preserving the original LRC structure. Moreover, under identical code parameters, TFR-LRC achieves improved fault tolerance, tolerating up to $g+2$ failures versus $g+1$ in conventional LRCs, with minimal additional cost. Notably, in maintenance mode, where entire racks may become temporarily unavailable, TFR-LRC demonstrates substantially better recovery efficiency compared to existing LRC schemes, making it a practical choice for real-world deployments. Full article

Aiming at the problems that traditional maintenance task scheduling schemes for damaged equipment have, poor adaptability to changes in uncertain factors and difficult-to-deal-with emergency scenarios, this paper proposes a maintenance task scheduling method for battle-damaged equipment based on digital twin (DT) and robust optimization. The purpose is to realize the dynamic synchronization between physical entities and virtual models through DT technology, and to leverage the anti-interference characteristics of robust optimization. The method involves constructing a multi-objective optimization model that maximizes the comprehensive importance of damaged equipment and minimizes maintenance time, and solving the model using the discrete particle swarm optimization (DPSO) algorithm. Simulation results show that this method can improve the efficiency of maintenance scheduling and the anti-interference ability in emergency situations. Through the comparison of three indicators, DT-DPSO performs the best in the maintenance scheduling of battle-damaged equipment: its convergence speed is 33.3% faster than that of DPSO and 20% faster than that of DT-non-dominated sorting genetic algorithm II (DT-NSGAII); its robustness is 16.3% higher than that of DPSO and 10.7% higher than that of DT-NSGAII; its dynamic reallocation speed is more than 40% faster than that of DPSO and more than 30% faster than that of DT-NSGAII. This method is suitable for maintenance scheduling requirements of high speed, stability, and anti-interference. Full article

The demand for robust safety monitoring in underground coal mines is increasing, yet traditional methods face limitations in long-term stability due to inadequate energy supply and high maintenance requirements. To address the critical challenges of high computational demand and energy constraints in this resource-limited environment, this paper proposes MineVisual, a battery-free visual sensing scheme specifically designed for underground coal mines. The core of MineVisual is an optimized lightweight deep neural network employing depthwise separable convolution modules to enhance computational efficiency and reduce energy consumption. Crucially, we introduce an energy-aware dynamic pruning network (EADP-Net) ensuring a sustained inference accuracy and energy efficiency across fluctuating power conditions. The system integrates supercapacitor buffering and voltage regulation for stable operation under wind intermittency. Experimental validation demonstrates that MineVisual achieves high accuracy (e.g., 91.5% Top-1 on mine-specific tasks under high power) while significantly enhancing the energy efficiency (reducing inference energy to 6.89 mJ under low power) and robustness under varying wind speeds. This work provides an effective technical pathway for intelligent safety monitoring in complex underground environments and conclusively proves the feasibility of battery-free deep learning inference in extreme settings like coal mines. Full article

Smallholder irrigation farmers play a vital role in sustaining rural communities in South Africa. However, the performance of smallholder irrigators, both as income generators and job creators, has come under scrutiny in recent years. In Limpopo province, a study was conducted in the Vhembe District using cross-sectional data from 95 independent and 165 public smallholder irrigators, which are privately established farmers and users of government-supported and managed irrigation systems, respectively. Qualitative data were collected through questionnaires, key informant interviews, and group discussions. Quantitative data were analyzed by SPSS version 30 using themes and codes, employing inferential statistical methods such as chi-square and t-tests to assess variables related to agrifood systems, crop selection, and market access. The study found that smallholders predominantly favor the production of grains, vegetables, and horticultural crops, with a statistically significant (p < 0.05) similarity between independent and public irrigators. Public irrigators dominate within irrigation schemes at 64% of the total, with X² of 22.7 with 0.001 p-value. Amongst the groups, the income distribution shows a statistically significant difference in earnings between independent and public irrigators (χ² = 25.83, p < 0.001). Informal and formal markets are accessible and available to 59% of independent irrigators, but 30% of public irrigators only access the informal market (p < 0.001). The major identified challenge across all smallholders is the lack of food value addition and commercial packaging. The study recommends the development of food value addition initiatives, adoption of climate-smart practices, maintenance of infrastructure, and improvement of market access to enhance productivity and sustainability. Full article

In recent years, solar energy has emerged as a pillar of sustainable development. However, maintaining panel efficiency under extreme environmental conditions remains a persistent hurdle. This study introduces an automated defect detection pipeline that leverages deep learning and computer vision to identify five standard anomaly classes: Non-Defective, Dust, Defective, Physical Damage, and Snow on photovoltaic surfaces. To build a robust foundation, a heterogeneous dataset of 8973 images was sourced from public repositories and standardized into a uniform labeling scheme. This dataset was then expanded through an aggressive augmentation strategy, including flips, rotations, zooms, and noise injections. A YOLOv11-based model was trained and fine-tuned using both fixed and adaptive learning rate schedules, achieving a mAP@0.5 of 85% and accuracy, recall, and F1-score above 95% when evaluated across diverse lighting and dust scenarios. The optimized model is integrated into an interactive dashboard that processes live camera streams, issues real-time alerts upon defect detection, and supports proactive maintenance scheduling. Comparative evaluations highlight the superiority of this approach over manual inspections and earlier YOLO versions in both precision and inference speed, making it well suited for deployment on edge devices. Automating visual inspection not only reduces labor costs and operational downtime but also enhances the longevity of solar installations. By offering a scalable solution for continuous monitoring, this work contributes to improving the reliability and cost-effectiveness of large-scale solar energy systems. Full article

Most of the failures during the actual operation of equipment are caused by improper human operation, tools, spare parts, and environmental factors. These faults are routine. Conventional faults have been validated during equipment development, testing, identification, and maintenance processes, with clear definitions and clear fault tree analysis (FTA) conclusions. Digital twins can offer rapid and interactive diagnostic capabilities for routine equipment failures. To enhance the efficiency of routine fault diagnosis and the interactive experience of the diagnosis process, this paper proposes a digital twin-based equipment routine fault diagnosis model. On this basis, considering the excellent interactivity of the Interactive Electronic Technical Manual (IETM), a conventional equipment fault diagnosis scheme based on twin data and IETM is designed. This scheme converts the equipment fault tree into an IETM fault data model (DM), which is structured and stored in a database to form a fault database. Using real-time twin data of equipment as input, the FTA method is adopted to perform step-by-step fault diagnosis and isolation guidance operation through the IETM process DM combined with fault, while providing maintenance operation guidance. When the real-time twin data of the equipment is not completely consistent with the fault information in the fault library, the case analysis method is used to calculate the similarity between the real-time twin data of the equipment and the clearly defined fault symptom information in the fault library. Based on the set similarity threshold, IETM pushes fault DMs above the threshold for corresponding fault diagnosis isolation guidance. Full article

Erosive wear from suspended sediments significantly threatens the structural integrity and efficiency of composite tidal turbine blades. This study develops a novel framework for predicting erosion in FR4 glass fibre-reinforced polymers (GFRPs)—materials increasingly adopted for marine renewable energy components. While erosion models exist for metals, their applicability to heterogeneous composites with unique failure mechanisms remains unvalidated. We calibrated the Oka erosion model specifically for FR4 using a complementary experimental–computational approach. High-velocity slurry jet tests (12.5 m/s) were conducted at a 90° impact angle, and erosion was quantified using both gravimetric mass loss and surface profilometry. It revealed a distinctive W-shaped erosion profile with 3–6 mm of peak material removal from the impingement centre. Concurrently, CFD simulations employing Lagrangian particle tracking were used to extract local impact velocities and angles. These datasets were combined in a constrained nonlinear optimisation scheme (SLSQP) to determine material-specific Oka model coefficients. The calibrated coefficients were further validated on an independent 45° impingement case (same particle size and flow conditions), yielding 0.0143 g/h predicted versus 0.0124 g/h measured (15.5% error). This additional case confirms the accuracy and feasibility of the predictive model under input conditions different from those used for calibration. The calibrated model achieved strong agreement with measured erosion rates (R² = 0.844), successfully capturing the progressive matrix fragmentation and fibre debonding, the W-shaped erosion morphology, and highlighting key composite-specific damage mechanisms, such as fibre detachment and matrix fragmentation. By enabling the quantitative prediction of erosion severity and location, the calibrated model supports the optimisation of blade profiles, protective coatings, and maintenance intervals, ultimately contributing to the extended durability and performance of tidal turbine systems. This study presents a procedure and the output of calibration for the Oka erosion model, specifically for a composite material, providing a transferable methodology for erosion prediction in GFRPs subjected to abrasive marine flows. Full article

In this work, a fault detection method and a distributed consensus fault-tolerant control (FTC) scheme are proposed for multiple quadrotor unmanned aerial vehicles (multi-QUAVs) with actuator faults. In order to identify the actuator faults in time, an auxiliary state observer is constructed first. Subsequently, a fault detection scheme based on the observer error is presented, which can improve the early warning ability of the multi-QUAVs. Meanwhile, to handle unknown sudden faults, the Nussbaum function approach is combined with the consensus theory to design a distributed consensus FTC strategy for multi-QUAVs. Compared with the traditional direct fault estimation method using the projection function technique, the proposed Nussbaum-based FTC method can avoid the singularity problem of the controller in a simple way. Moreover, all error signals of the closed-loop system are proved to be uniformly ultimately bounded via Lyapunov stability theory and the consensus control algorithm. Finally, simulation comparison results indicate the early warning capability of the fault detection method and the formation maintenance performance of the developed fault-tolerant controller. Full article

Due to the high cost of hydrogen utilization and the uncertainties in renewable energy generation and load demand, significant challenges are posed for the operation optimization of hydrogen-containing integrated energy systems (IESs). In this study, a robust operational model for an electric–heat–gas IES (EHG-IES) is proposed, considering the hydrogen energy system heat recovery (HESHR) and multiple uncertainties. Firstly, a heat recovery model for the hydrogen system is established based on thermodynamic equations and reaction principles; secondly, through the constructed adjustable robust optimization (ARO) model, the optimal solution of the system under the worst-case scenario is obtained; lastly, the original problem is decomposed based on the column and constraint generation method and strong duality theory, resulting in the formulation of a master problem and subproblem with mixed-integer linear characteristics. These problems are solved through alternating iterations, ultimately obtaining the corresponding optimal scheduling scheme. The simulation results demonstrate that our model and method can effectively reduce the operation and maintenance costs of HESHR-EHG-IES while being resilient to uncertainties on both the supply and demand sides. In summary, this study provides a novel approach for the diversified utilization and flexible operation of energy in HESHR-EHG-IES, contributing to the safe, controllable, and economically efficient development of the energy market. It holds significant value for engineering practice. Full article

Smallholder farmers play a pivotal role in food production and rural development in South Africa. However, their productivity is often constrained by reliance on rainfed agriculture and the underutilisation of innovative technologies such as irrigation systems. This study assessed the impact of innovative irrigation system (IIS) use on crop yield among smallholder crop farmers (SCFs) in Mbombela Local Municipality. Focusing on vegetables and agronomic crop producers. Primary data was collected from 308 SCFs using a structured questionnaire through descriptive and cross-sectional survey design. A Probit regression model was used to estimate the probability of using an IIS, while Propensity Score Matching (PSM) estimated the average treatment effect on the treated (ATT) in terms of yield. The results reveal that age group (p = 0.080), main source of off-farm income (p = 0.042), and high input costs (p = 0.006) significantly determined IIS use. Impact analysis confirms that users of IISs achieved higher yields than non-users. The study concludes that innovative irrigation technologies can significantly improve smallholder productivity. It recommends that policymakers and government bodies prioritise scaling up access to IIS, introduce subsidies or low-interest financing schemes to alleviate the IIS usage costs, and strengthen extension services to provide targeted training on irrigation scheduling, system maintenance, and water-use efficiency. Full article