Search Results (186)

Evaluating the competitiveness of electricity is the most important issue. The main aim of this study was to determine the cost-effectiveness of renewable energy production in the European Union (EU) using the levelized cost competitiveness of renewable energy sources. The weighted average cost of capital (WACC) for onshore wind was calculated for European (EU) countries. The levelized cost of electricity (LCOE) approach was used to evaluate the energy costs of renewable energy sources. Energy production costs were compared across different technologies. The capital expenditures associated with solar PV are expected to decrease from USD 810/kW in 2021 to USD 360/kW in 2050. The power factor will remain stable at 14% during the analyzed period. Fuel, CO₂, and operation and maintenance (O&M) costs will be maintained at USD 10/MWh at all three time points of the analysis (2021, 2030, and 2050), whereas the LCOE will decrease from USD 50/MWh in 2021 to USD 25/MWh in 2050. The capital expenditures associated with onshore wind energy will decrease from USD 1590/kW in 2021 to USD 1410/kW in 2050. The power factor will increase from 29% to 30%, and fuel, CO₂, and O&M costs will reach USD 15/MWh in all three years. The LCOE will decrease from USD 55/MWh in 2021 to USD 45/MWh in 2050. In offshore wind projects, capital expenditures are expected to decrease considerably from USD 3040/kW in 2021 to USD 1320/kW in 2050. Full article

As a crucial component in the evolution of modern warfare toward digitization and intelligentization, unmanned aerial vehicle (UAV) equipment requires a more precise and efficient operation and maintenance (O&M) system. Based on the Department of Defense Architecture Framework (DoDAF) 2.0, the integration of Multi-Agent Systems (MAS) and military simulation technology provides a comprehensive, rational, and feasible theoretical foundation for the construction and validation of an intelligent O&M system for UAV equipment. Firstly, starting from the O&M tasks of UAV equipment in intelligent warfare, this study analyzes the capability requirements for intelligent UAV O&M by following the generation path of scenarios, activities, and capabilities. Three core capabilities are proposed: situational awareness, decision support, and mission execution. Secondly, various O&M tasks are decomposed into behaviors of multiple types of agents, and based on this, an intelligent O&M system for UAV equipment is designed using a “cloud-edge-terminal” distributed architecture. Finally, simulations are conducted to model and validate UAV equipment maintenance tasks. Experimental results demonstrate that the MAS-based UAV O&M system significantly enhances support efficiency, accuracy, and response speed, offering a novel solution for O&M in future UAV operations. Full article

Wind turbine operation and maintenance (O&M) faces significant challenges due to the complexity of equipment, harsh operating environments, and the difficulty of real-time fault prediction. Traditional methods often fail to provide timely and accurate warnings, leading to increased downtime and maintenance costs. To address these issues, this study systematically explores an intelligent operation and maintenance method for wind turbines, utilizing digital twin technology and multi-source data fusion. Specifically, it proposes a remote intelligent operation and maintenance (O&M) framework for wind turbines based on digital twin technology. Furthermore, an algorithm model for multi-source operational data analysis of wind turbines is designed, leveraging a Whale Optimization Algorithm-optimized Temporal Convolutional Network with an Attention mechanism (WOA-TCN-Attention). The WOA is used to optimize the hyperparameters of the TCN-Attention model. Then, the gearbox fault alarm threshold and warning threshold are set using the statistical characteristics of the residual values, and the absolute value of the residuals is used to determine the abnormal operating state of the gearbox. Finally, the proposed method was validated using operational data from a wind farm in Xinjiang. With input data from multiple sources, including seven key parameters such as temperature, pressure, and power, the method was evaluated based on EMAE, ERMSE, and EMAPE. The results demonstrated that the proposed method achieved the smallest prediction error and provided effective early warnings 18 h and 33 min prior to actual failures, enabling real-time and efficient operation and maintenance management for wind turbines. Full article

Operation and maintenance (O&M) activities represent a significant share of the levelized cost of energy (LCOE) for offshore wind farms (OWFs), making cost reduction a key priority. Robotic-based solutions, leveraging aerial and underwater vehicles in a cooperative framework, offer the potential to optimize O&M logistics and reduce costs. Additionally, the deployment of persistent autonomous robotic systems can minimize the need for human intervention, enhancing efficiency. This study presents the development of an O&M cost calculator that integrates multiple modules: a weather forecast module to account for meteorological uncertainties, a failure module to model OWF failures, a maintenance module to estimate costs for both planned and unplanned activities, and a power module to quantify downtime-related losses. A forward-looking comparative economic analysis is conducted, assessing the cost-effectiveness of human-based versus robot-based inspection, maintenance, and repair (IMR) activities. The findings highlight the economic viability of robotic solutions in offshore wind O&M, supporting their potential role in reducing operational expenditures and improving energy production efficiency. Full article

This study proposes a simplified mathematical formulation for optimizing isolated microgrids, enhancing computational efficiency while preserving solution quality. The research focuses on the influence of Operation and Maintenance (O&M) costs for Non-Dispatchable Generators (NDGs) and the relationship between costs and pollutant emissions. The proposed simplification reduces computational requirements, improves result interpretability, and increases the scalability of optimization techniques. The O&M costs of photovoltaic and wind systems were excluded from the initial optimization and calculated afterward. A Student’s t-test yielded a p-value of 87.3%, confirming no significant difference between the tested scenarios, ensuring that the simplification does not impact solution quality while reducing computational complexity. For emission-related costs, scenarios with single and multiple pollutant generators were analyzed. When only one generator type is present, modifications are needed to enable effective multi-objective optimization. To address this, two alternative mathematical formulations were tested, offering more suitable approaches for the problem. However, when multiple pollutant sources exist, cost and emission differences naturally define the problem as multi-objective without requiring adjustments. Future work will explore grid-connected microgrids and additional optimization objectives, such as loss minimization, voltage control, and device lifespan extension. Full article

Aging buildings pose a significant concern for many large developed cities, and the operation and maintenance (O&M) of mechanical, electrical, and plumbing (MEP) systems becomes critical. Building Information Modeling (BIM) facilitates efficient O&M for MEP. However, these numerous aging buildings were constructed without BIM, making BIM reconstruction a monumental undertaking. This research proposes an automatic approach for generating BIM based on 2D drawings. Semantic segmentation was utilized to identify MEP components in the drawings, trained on a custom-made MEP dataset, achieving an mIoU of 92.18%. Coordinates and dimensions of components were extracted through contour detection and bounding box detection, with pixel-level accuracy. To ensure that the generated components in BIM strictly adhere to the specifications outlined in the drawings, all model types were predefined in Revit by loading families, and an MEP component dictionary was built to match dimensions and model types. This research aims to automatically and efficiently generate BIM for MEP systems from 2D drawings, significantly reducing labor requirements and demonstrating broad application potential in the large-scale O&M of numerous aging buildings. Full article

To enhance the efficiency of reusing massive unstructured operation and maintenance (O&M) data generated during routine railway maintenance inspections, this paper proposes a Named Entity Recognition (NER) method that integrates multi-granularity semantics and a Multi-Scale Retention (MSR) mechanism. The proposed approach effectively transforms expert knowledge extracted from manually processed fault data into structured triplet information, enabling the in-depth mining of track circuit O&M text data. Given the specific characteristics of railway domain texts, which include a high prevalence of technical terms, ambiguous entity boundaries, and complex semantics, we first construct a domain-specific lexicon stored in a Trie tree structure. A lexicon adapter is then introduced to incorporate these terms as external knowledge into the base encoding process of RoBERTa-wwm-ext, forming the lexicon-enhanced LE-RoBERTa-wwm model. Subsequently, a hidden feature extractor captures semantic representations from all 12 output layers of LE-RoBERTa-wwm, performing weighted fusion to fully leverage multi-granularity semantic information across encoding layers. Furthermore, in the downstream processing stage, two computational paradigms are designed based on the MSR mechanism and the Regularized Dropout (R-Drop) mechanism, enabling low-cost inference and efficient parallel training. Comparative experiments conducted on the public Resume and Weibo datasets demonstrate that the model achieves F1 scores of 96.75% and 72.06%, respectively. Additional experiments on a track circuit dataset further validate the model’s superior recognition performance and generalization capability. Full article

Long-distance sewerage network systems have serious vulnerabilities, specifically pipeline blockage, leakage, sedimentation, mixed connection, and other problems. A vulnerability evaluation system for a sewage network was established in this study with the comprehensive consideration of three aspects: basic attributes of the sewage network, operation and maintenance (O&M) drivers, and structural level. First, we obtained vulnerability indicators for the sewage pipeline network system through data collection and the preliminary selection and screening of indicators. The extent of the importance of each criterion level to the vulnerability was clarified through principal component analysis (PCA), with the basic attribute indicators being the per capita GDP (X3) and the urbanization rate (X5), the O&M-driven indicators being the daily per capita wastewater treatment volume (X7) and the industrial wastewater discharge volume (X8), and the structural-level indicators being the pipe diameter (X13) and the flow capacity (X15). Qingshanhu District, Jiangxi province, was taken as an example for diagnosing and evaluating vulnerability. Using the ranking size of PCA indicators as the evaluation level of the importance for the analytic hierarchy process (AHP) indicators, a hierarchical structure model was established. The evaluation value was obtained by weighting the hierarchical structure model results with the scores of each indicator. The comprehensive evaluation values of basic attributes, operation and maintenance drivers, and structural level were 58.38, 68.67, and 73.17, which corresponded to vulnerability levels of III, II, and II, respectively. Full article

This study proposes a comprehensive framework for digitalizing and managing assets with low initial digital maturity, focusing on their operation and maintenance (O&M) lifecycle. The framework integrates Internet of Things (IoT) networks with Asset Health Index (AHI) models through four interconnected components. The Asset Definition Model ensures standardized data representation based on IEC 81346-1:2022 and ISO 14224:2016, while the Asset Criticality Model prioritizes maintenance actions using risk-informed analysis. The Asset Monitoring Model enables real-time data acquisition through IoT sensors, facilitating condition-based monitoring and dynamic decision-making. Finally, the Intelligent Asset Management Models support long-term planning by simplifying data complexity and aligning with advanced maintenance strategies. A case study on bridge maintenance demonstrates the practical value of the framework, showcasing its ability to integrate real-time monitoring with predictive decision-making tools. By bridging asset monitoring and lifecycle planning, the framework enhances operational efficiency, reduces maintenance costs, and addresses the challenges posed by limited digital maturity in critical infrastructure. This approach represents a significant advancement in the digital transformation of maintenance management. Full article

In recent years, the applications of digital technologies in sustainable construction have gained increasing interest. However, no comprehensive literature review has been conducted. Thus, this paper analyzes 990 relevant articles in this regard published from 2014 to 2023 by using CiteSpace (version 6.3.R1) and HistCite (version Pro 2.1) and identifies the most influential journals, institutions, and regions. The knowledge base was detected through a cluster analysis, which concentrates more on seven core themes: barriers, energy efficiency and building energy performance, life cycle assessment, computer vision, renovation, building sustainability assessment, and management. A citation analysis revealed that the applications of digital technologies were based in four dimensions of sustainable construction: environmental, social, and economic performance and green building assessment are the current hotspots. Finally, the potential future research trends in this field were proposed: (1) strengthening research on the application of more digital technologies; (2) expanding the use of digital technologies in the Operation and Maintenance (O & M) and demolition phases; (3) deepening the research on multi-objective optimization; and (4) exploring how to overcome obstacles. The findings provide highly valuable information for researchers with current research ideas and future directions in this field. This paper also has the potential to deepen practitioners’ comprehension of optimal digital technologies for bolstering construction sustainability. Full article

The rapid expansion of civil infrastructure in China underscores the critical need for advanced solutions to ensure the structural health of aging bridges. This study introduces a novel data and knowledge-driven digital twin modeling (DK-DTM) framework designed to enhance the safe and efficient operation and maintenance (O&M) of bridges. Such a system should be capable of (1) monitoring structural dynamics in real time, (2) capturing spatiotemporal details and changes (e.g., defects and deformations), (3) analyzing structure deterioration patterns, (4) predicting structure failure risks, and (5) generating optimal maintenance and repair actions for ensuring structural safety. Previous studies have developed advanced sensing techniques and robust artificial intelligence algorithms for capturing and analyzing bridge health conditions. However, most existing techniques and algorithms heavily rely on high-quality data, which are difficult to obtain during bridge O&M. This raises the critical question of how to incorporate expert knowledge together with data-driven tools to establish a trustworthy DT for bridge O&M. This study presents the DK-DTM framework, which uniquely integrates multi-source data collection, spatiotemporal modeling, and expert knowledge reasoning. By combining these components, the framework supports smart structural health assessments of bridges, enabling comprehensive monitoring, prediction, and decision-making for efficient maintenance. The spatial and temporal models provide real-time data, while the expert knowledge model functions as an automated evaluation tool for structural health assessment. The results demonstrate that the proposed DK-DTM framework significantly enhances the accuracy and efficiency of O&M processes for aging bridges, addressing key gaps in existing digital twin methodologies. Full article

The rapidly increasing installed capacity of Wind Turbines (WTs) worldwide emphasizes the need for Operation and Maintenance (O&M) strategies favoring high availability, reliability, and cost-effective operation. Optimal decision-making and planning are supported by WT health condition analyses based on data from the Supervisory Control and Data Acquisition (SCADA) system. However, SCADA data are highly imbalanced, with a predominance of healthy condition samples. Although this imbalance can negatively impact analyses such as detection, Condition Monitoring (CM), diagnosis, and prognosis, it is often overlooked in the literature. This review specifically addresses the problem of SCADA data imbalance, focusing on strategies to mitigate this condition. Five categories of such strategies were identified: Normal Behavior Models (NBMs), data-level strategies, algorithm-level strategies, cost-sensitive learning, and data augmentation techniques. This review evidenced that the choice among these strategies is mainly dictated by the availability of data and the intended analysis. Moreover, algorithm-level strategies are predominant in analyzing SCADA data because these strategies do not require the costly and time-consuming task of data labeling. An extensive public SCADA database could ease the problem of abnormal data scarcity and help handle the problem of data imbalance. However, long-dated requests to create such a database are still unaddressed. Full article

As an emerging technology, digital twin (DT) is increasingly valued in bridge management for its potential to optimize asset operation and maintenance (O&M). However, traditional bridge management systems (BMS) and existing DT applications typically rely on standalone building information modeling (BIM) or geographic information system (GIS) platforms, with limited integration between BIM and GIS or consideration for their underlying graph structures. This study addresses these limitations by developing an integrated DT system that combines WebGIS, WebBIM, and graph algorithms within a three-layer architecture. The system design includes a common data environment (CDE) to address cross-platform compatibility, enabling real-time monitoring, drone-enabled inspection, maintenance planning, traffic diversion, and logistics optimization. Additionally, it features an adaptive data structure incorporating JSON-based bridge defect information modeling and triple-based roadmap graphs to streamline data management and decision-making. This comprehensive approach demonstrates the potential of DTs to enhance bridge O&M efficiency, safety, and decision-making. Future research will focus on further improving cross-platform interoperability to expand DT applications in infrastructure management. Full article

Leading edge (LE) erosion is a type of damage that inhibits the aerodynamic performance of a wind turbine, resulting in high operation and maintenance (O&M) costs. This paper makes use of a small dataset consisting of 50 images of LE erosion and healthy blades for feature extraction and the training of four types of classifiers, namely, support vector machine (SVM), random forest, K-nearest neighbour (KNN), and multi-layer perceptron (MLP). Six feature extraction methods were used with these classifiers to train 24 models. The dataset has also been used to train a convolutional neural network (CNN) model developed using Keras. The purpose of this work is to determine whether classical machine learning (ML) classifiers trained with extracted features can produce higher-accuracy results, train faster, and classify faster than deep learning (DL) models for the application of LE damage detection of wind turbine blades. The oriented fast and rotated brief (ORB)-trained SVM achieved an accuracy of 90% ± 0.01, took 80.4 s to train, and achieved inference speeds of 63 frames per second (FPS), compared to the CNN model, which achieved an accuracy of 79.4% ± 2.07, took 4667.4 s to train, and achieved an inference speed of 1.3 FPS. These results suggest that classical ML models can be more accurate and efficient than DL models if the appropriate feature extraction method is used. Full article

This study presents an integrated methodology for evaluating operations and maintenance (O&M) costs for floating offshore wind turbines (FOWTs), incorporating vessel motion dynamics. By combining UWiSE, a discrete-event simulation tool, with SafeTrans, a voyage simulation software, vessel motion effects during offshore operations are modeled. The approach is demonstrated in a case study at two wind farm sites, Marram Wind and Celtic Sea C. Three major component replacement (MCR) strategies were assessed: Tow-to-Port (T2P), Floating-to-Floating (FTF), and Self-Hoisting Crane (SHC). The T2P strategy yielded the highest O&M costs—94 kEUR/MW/year at Marram Wind and 97 kEUR/MW/year at Celtic Sea C—due to the extended MCR durations (90–180 days), leading to lower availability (90–94%). In contrast, the FTF and SHC strategies offered significantly lower costs and downtime. The SHC strategy was most cost-effective, reducing costs by up to 64% while achieving 97–98% availability. The integrated approach was found to be either more restrictive or more permissive depending on the specific sea states influencing the motion responses. This variability highlights the critical role of motion-based dynamics in promoting safe and efficient O&M practices, particularly for advancing FOWT operations. Full article