Maintenance Costs Research Articles

Water Reuse: Contribution of a Decision Support Model

Sustainability seeks to strike a balance between preserving the environment and meeting human needs without compromising future generations. In this context, and considering the effects of climate change on water availability, water reuse is emerging as an alternative to conventional water sources for various purposes, contributing to sustainability. Water reuse projects are, in general, not simple to implement due to different technical, environmental, social and economic aspects. In this paper, a support decision model for water reuse projects is presented, identifying relevant indicators and parameters. Based on a literature review, four indicators or dimensions (technical, social, environmental and economic) and twelve parameters (e.g., WWTP safety, transport complexity, existence of legislation, risk to health and environment, energy consumption, degree of acceptance and required investment and operation and maintenance costs) are proposed. The Fuzzy Analytic Hierarchy Process (FAHP) method is used as a component of the model to determine the weights of the indicators and parameters in order to allow the calculation of a reuse feasibility index (RFI). The developed model was applied to the city of Aquiraz, Ceará, Brazil, and the RFI found was 82%, which means that the water reuse project had very high viability. The results underwent a sensitivity analysis, which confirms the consistency of the conclusions.

Understanding Islandness Effects Through the Challenges of Water Infrastructure: A Case Study on the Kinmen Islands

On Taiwan’s offshore islands of Kinmen, the water supply infrastructure has experienced various challenges stemming from the physical and social conditions of islands. These conditions are related to islands’ distinct characteristics, such as smallness, boundedness, and remoteness, collectively referred to as islandness. This paper combines perspectives from infrastructure and island studies to examine how islandness-related effects have contributed to infrastructure failures. Drawing from interviews and document analysis, it explores the issues faced by the reservoirs and the desalination plant in Kinmen, including small storage and poor water quality, lack of energy and technical capacities, high production and maintenance costs, and poor seawater quality. Then, based on the above case study and a document analysis of water supply technical reports on Taiwan’s offshore islands, some shared islandness-related conditions are identified and categorized in relation to smallness, remoteness and peripherality, and ocean materiality. While offering generalizability, it is crucial to note that these conditions are not seen as determined but rather as relational and contingent upon other factors. Through such examination, the paper contributes to the discussion surrounding islandness and infrastructure breakdowns, shedding lights on the relational effects of islandness on water supply infrastructure.

Time Series Data Augmentation for Energy Consumption Data Based on Improved TimeGAN

Predicting the time series energy consumption data of manufacturing processes can optimize energy management efficiency and reduce maintenance costs for enterprises. Using deep learning algorithms to establish prediction models for sensor data is an effective approach; however, the performance of these models is significantly influenced by the quantity and quality of the training data. In real production environments, the amount of time series data that can be collected during the manufacturing process is limited, which can lead to a decline in model performance. In this paper, we use an improved TimeGAN model for the augmentation of energy consumption data, which incorporates a multi-head self-attention mechanism layer into the recovery model to enhance prediction accuracy. A hybrid CNN-GRU model is used to predict the energy consumption data from the operational processes of manufacturing equipment. After data augmentation, the prediction model exhibits significant reductions in RMSE and MAE along with an increase in the R2 value. The prediction accuracy of the model is maximized when the amount of generated synthetic data is approximately twice that of the original data.

Metabolic Performance of Mealworms and Black Soldier Fly Larvae Reared on Food and Agricultural Waste and By-Products

Mealworms and black soldier fly (BSF) larvae are two of the most reared insects at an industrial scale. Both may feed on by-products from agricultural and food industries. Feed efficiency is one of the most important aspects of such processes and varies between species and feed substrates and depends on the metabolic performance of the larvae. Compared to each other, both species may hold advantageous capabilities affecting their feed efficiency, likely depending on the feed substrate. We reared mealworms and BSF larvae on a diverse selection of by-products from agricultural and food industries, quantified major metabolic rates across their life spans, and compared their performances. The type of feed substrates had stronger effects on the growth of mealworms than on black soldier fly larvae. Generally, BSF larvae were advantageous in terms of the highest maximal specific growth rate (0.50–0.77 day−1) and feed assimilation rate (0.81–1.16 day−1) and shortest development period (23–25 days) but disadvantageous in terms of metabolic maintenance cost (0.07–0.21 day−1). In mealworms, the maximal specific growth rates were 0.02–0.11 day−1, the highest feed assimilation rates were 0.16–0.37 day−1, and the development period was at least 65–93 days, while maintenance was only 0.02–0.05 day−1. In contrast to the BSF larvae, the specific maintenance rate was weight dependent in the mealworms and lowest in the largest individuals. The combined outcome of these metabolic rates resulted in an average carbon net growth efficiency, NGE*avg of 0.16–0.40 in mealworms and 0.33–0.56 in BSF larvae across their life span. It thus seems that BSF larvae are more versatile and somewhat more efficient at converting diverse feed substrates into growth than mealworms. Differences in NGE*avg affected the substrate conversion efficiencies (i.e., the ratio of the weight gain of the larvae to the reduced weight of feed substrates) and may thus impact the overall outcome of insect farming.

Fault Diagnosis and Optimized Operation and Maintenance of Drop-out Fuses in Alpine Regions Using MKL-SVM and GRU

Extreme climate in alpine regions leads to frequent failures of drop-out fuses, but existing methods have low detection accuracy and response speed. This article combines MKL-SVM (Multiple Kernel Learning-Support Vector Machine) and GRU (Gated Recurrent Unit) model to achieve precise fault diagnosis of drop-out fuses, leveraging their efficient processing of high-dimensional time series data. Sensors are used to collect data such as voltage, current, temperature, and wind speed. Based on GRU, global trends, temporal hidden states, and dynamic nonlinear features of the data are extracted as inputs for subsequent MKL-SVM. In MKL-SVM, a linear kernel function is used to simplify the linear relationship between static features; targeting temporal features, RBF (Radial Basis Function) kernel effectively captures dynamic trends, fluctuations, and periodic changes in temporal features. To further enhance the classification ability of the model, the kernel function parameters, including linear kernel weights and RBF kernel widths, are adjusted through cross-validation. Finally, based on the model diagnosis results and predicted data, an optimized operation and maintenance plan is formulated to reduce maintenance costs and improve equipment operating efficiency. The results show that the fusion model can significantly improve the reliability of fault detection and adapt to the unique environmental challenges of alpine regions. The accuracy of fault diagnosis for drop-out fuses is 95%, with an MSE (mean square error) value of about 0.15 and a maintenance cost reduction of about 21.89%. This method can achieve accurate fault diagnosis and efficient optimization of operation and maintenance under extreme weather conditions.

A model of optimal digestive strategy in infrequently-feeding snakes

Abstract Organisms require energy for survival, growth, and reproduction. In a system with a finite energy supply, fluctuations in resource availability can select for plasticity in the allocation of resources between competing physiological processes. Infrequently-feeding snakes, which naturally experience extended episodes of fasting, have evolved the capacity to modulate gastrointestinal (GI) performance in response to changes in digestive load. Specifically, the gut can be downregulated during fasting to reduce metabolic maintenance costs. Some energy is, however, required to upregulate the digestive system again to allow the exploitation of captured prey. Despite significant inquiry into the relationship between sit-and-wait foraging tactics and GI plasticity, quantitative examination of the optimal digestive strategy for an infrequently-feeding snake is lacking. Here, we construct an optimisation model to quantitatively predict this strategy in terms of the length of time post-feeding after which gut downregulation occurs and the minimum prey mass required to initiate gut upregulation. Contrary to long-held assertions, our simulations predict that infrequently-feeding snakes of all sizes that practice gut downregulation benefit from consuming relatively small prey. We identify gut downregulation as an adaptive strategy when prey are small and encountered infrequently, and assert it as a critical factor in determining predator vulnerability to food scarcity. When parameterising the model, we found the distribution of potential prey body sizes to be poorly characterised in the literature. Accordingly, we identify the frequency distribution of individual body sizes within a terrestrial community as a key focus of future ecological research.

A bond strength analysis of carboncor asphalt layer on road surfaces

Transport infrastructure is a critical component of socio-economic development, particularly in rapidly urbanizing Vietnam, where increasing traffic congestion poses significant challenges. Road surface quality and durability are essential factors in ensuring the efficiency and longevity of the transportation system. Carboncor Asphalt (CA) material, with its superior resistance to cracking, water damage, and harsh weather conditions, is a promising solution for road surface construction in Vietnam. However, there is a limitation of research on the mechanical behavior of carboncor asphalt in the specific climatic and environmental conditions of the country. To address this knowledge gap, this study investigates the shear behavior of a carboncor asphalt layer interfaced with both asphalt concrete and cement concrete surface layers. A comprehensive range of laboratory tests is conducted to assess the bond strength between the carboncor asphalt layer and the overlying road surfaces. Results indicate a negative correlation between temperature and bond strength for the overlay carboncor asphalt layer on both asphalt and cement concrete substrates. Notably, bond strength demonstrated a significant increase over time. The findings in this study are expected to have significant implications for road construction and maintenance using CA overlay in Vietnam. By providing a scientific foundation for material selection, design, and construction of road surfaces tailored to local conditions, this study aims to enhance investment efficiency and reduce maintenance costs

Study on corrosion monitoring and assessment method of reinforced concrete based on multi-sensor fusion

Purpose The purpose of this paper is to investigate the accurate monitoring and assessment of steel bar corrosion in concrete based on deep learning multi-sensor information fusion method. The paper addresses the issue of traditional corrosion assessment models relying on sufficient data volume and low evaluation accuracy under small sample conditions. Design/methodology/approach A multi-sensor integrated corrosion monitoring equipment for reinforced concrete is designed to detect corrosion parameters such as corrosion potential, current, impedance, electromagnetic signal and steel bar stress, as well as environmental parameters such as internal temperature, humidity and chloride ion concentration of concrete. To overcome the small amount of monitoring data and improve the accuracy of evaluation, an improved Siamese neural network based on the attention mechanism and multi-loss fusion function is proposed to establish a corrosion evaluation model suitable for small sample data. Findings The corrosion assessment model has an accuracy of 98.41%, which is 20% more accurate than traditional models. Practical implications Timely maintenance of buildings according to corrosion evaluation results can improve maintenance efficiency and reduce maintenance costs, which is of great significance to ensure structural safety. Originality/value The corrosion monitoring equipment for reinforced concrete designed in this paper can realize the whole process of monitoring inside the concrete. The proposed corrosion evaluation model for reinforced concrete based on Siamese neural network has high accuracy and can provide a more accurate assessment model for structural health testing.

Comparative economic analysis of batch vs. continuous manufacturing in catalytic heterogeneous processes: impact of catalyst activity maintenance and materials costs on total costs of manufacturing in the production of fine chemicals and pharmaceuticals

Abstract To evaluate the feasibility of transforming batch manufacturing processes in the fine chemicals and pharmaceutical industries to continuous synthesis, Capex, Opex and the total cost of manufacturing are estimated for production facilities for the hydrogenation of a nitro compound to its final amino counterpart. Two cases are evaluated: First, a high annual production dedicated plant, designed on the basis of processing 100,000 kg of the principal raw material per year, where raw materials cost, catalyst cost, and catalyst activity maintenance are varied over a broad range for typical industrial cases. Second, a “short campaign” model for a small volume production trial setup designed for the manufacture of only 100 kg of the final product, as a way to evaluate relevant industrial scenarios of scale-up and process development. A comparison is made between slurry batch, catalyst basket batch reactor and fixed bed continuous reactor manufacturing facilities. The hydrogenation of 2,4-dinitrotoluene was chosen as a probe reaction for the development of the manufacturing processes, with costs of the key raw material varying between $5 and $100 per kilogram, costs of catalyst varying between $100 and $1,500 per kilogram, and catalyst activity maintenances varying between 1,000 and 2,000,000 total turnovers before a change in catalyst load is necessary. For low catalyst activity maintenance, the total manufacturing costs for the fixed bed reactor process were always found to be higher than those of the two other alternatives. As catalyst activity maintenance increases, the manufacturing costs for the continuous alternative rapidly fall, reaching savings between 37 and 75% compared to the base batch reactor case, depending on the combination of costs of the key raw material and catalyst used. Graphical Abstract

Gold Nanoparticle Enhanced Polyurethane Elastomer with High-Efficiency Self-healing and Potent Mechanical Property

Thermoplastic polyurethanes (TPUs) are renowned for their superior mechanical properties, including elasticity, durability, and abrasion resistance, which make them indispensable in industries such as automotive, construction, and electronics. However, conventional TPUs often lack self-healing capabilities, limiting their lifespan and increasing maintenance costs. This study addresses this limitation by developing a novel TPU elastomer (PUAN-1) that incorporates gold nanoparticles (AuNPs) and disulfide bonds to enhance both mechanical strength and self-healing efficiency. The AuNPs serve as a reinforcing phase, improving the TPU's mechanical performance by strengthening the hard segments and promoting dynamic disulfide bond reformation through photothermal effects. This innovative approach enables the TPU to achieve rapid self-healing under mild conditions while maintaining excellent mechanical properties. Experimental evaluations demonstrated that PUAN-1 exhibited an ultimate tensile strength of 4.4 MPa, an elongation at break of 1165%, and a toughness of 39.7 MJ/m, significantly outperforming conventional TPUs. Additionally, PUAN-1 displayed a high self-healing efficiency, with over 88% recovery in mechanical properties within two hours of damage. Conversely, a higher concentration of AuNPs, as in PUAN-2, was found to hinder both self-healing and mechanical performance due to restricted chain mobility and suppressed microphase separation. These findings highlight the critical balance required in optimizing AuNP content to maximize both reinforcement and flexibility. The developed TPU elastomer presents a promising candidate for applications demanding durable, self-repairing materials, such as wearable electronics, flexible sensors, and biomedical devices. This study provides valuable insights into the design of advanced TPUs that combine high mechanical robustness with effective self-healing capabilities, offering significant potential for use in dynamic and demanding environments.

An Intelligent 5G Unmanned Aerial Vehicle Path Optimization Algorithm for Offshore Wind Farm Inspection

In recent years, clean energy has gained increasing attention, with offshore wind power playing a crucial role in global energy production. However, the high operating and maintenance costs of offshore wind farms remain a significant challenge. The advent of 5G technology provides a solution for efficiently monitoring and controlling wind power equipment. The use of 5G unmanned aerial vehicles (UAVs) for blade inspections is a promising development. A key challenge is efficiently planning UAV flight paths for fast and effective inspections in complex offshore environments. To address this problem, we conduct an in-depth study of the 5G UAV path optimization method. In this paper, the UAV inspection path problem is modeled as an obstacle avoidance traveling salesman problem (TSP), taking into full account UAV flight constraints and complex sea environment factors, particularly the impact of sea wind on UAV flight speed. We propose a novel Sea Wind-Aware Improved A*-Guided Genetic Algorithm (SWA-IAGA), which integrates an improved A* algorithm to guide the genetic algorithm for efficient path planning, with the assistance of relevant graphical knowledge. This algorithm overcomes the limitations of traditional single-path planning methods, enabling more accurate and efficient path planning.

Optimization of In-Motion EV Charging Infrastructure for Power Systems Using Generative Adversarial Network-Based Distributionally Robust Techniques

This paper presents an innovative optimization framework for the co-management of dynamic electric vehicle (EV) charging lanes and power distribution networks, addressing grid stability amidst fluctuating EV charging demands. Integrating generative adversarial networks (GANs) and distributionally robust optimization (DRO), the framework models uncertainties in traffic flow and renewable energy generation, optimizing system performance under worst-case conditions to mitigate risks of grid instability. Applied to a highway with eight dynamic charging lanes (500 kW per lane), serving up to 50 EVs simultaneously, the framework balances energy contributions from 15 renewable generators (60% of the mix) and 10 non-renewable generators. Simulation results highlight its effectiveness, maintaining grid stability with voltage deviations within 0.02 p.u., reducing energy losses to under 0.8 MW during peak traffic (1500 vehicles per hour), and achieving 95% lane utilization. Dynamic charging enabled EV users to save USD 0.08 per kilometer through reduced stationary charging downtime, optimized travel efficiency, and lower energy costs. Additionally, the system minimizes maintenance costs by optimizing lane and grid reliability. This study underscores the potential of GAN-based DRO methodologies to enhance the efficiency of power grids supporting dynamic EV charging, offering scalable solutions for diverse regions and traffic scenarios.

The Use of Reinforced Concrete for Durable and Aesthetic Sports and Recreational Sports

This study focuses on the design and construction of durable recreational seating using reinforced concrete, addressing the limitations of traditional materials such as wood and metal, which are prone to environmental degradation and high maintenance costs. Reinforced concrete is known for its durability, strength, and resistance to harsh weather conditions, making it an ideal material for public seating in outdoor environments. Several key tests were conducted to assess the performance of the concrete mix used in this project. Sieve analysis confirmed that the fine and coarse aggregates were well-graded, ensuring optimal compaction and strength. The slump test,conducted with a water-cement ratio of 0.6, produced a slump value of 150 mm, indicating medium workability. This level of workability is suitable for concrete that requires easy placement and proper compaction without segregation or excessive bleeding. The compressive strength test at 7 days yielded a compressive strength of 15.81 MPa, demonstrating the early strength development of the concrete. However, compressive strength results for the 14-day and 28-day tests are still pending, as the remaining cubes are currently in the curing tank. These results will provide further insights into the long-term strength and performance of the concrete once the curing process is completed. Preliminary findings suggest that reinforced concrete is an excellent choice for recreational seating due to its ability to provide long-lasting durability and reduced maintenance. The combination of well-graded aggregates, an optimal water-cement ratio, and promising early compressive strength indicates that this material will perform well under both static and dynamic loads typically encountered in public seating.

An uncertainty-aware deep learning ensemble approach for effective cutting tool predictive maintenance decision-making

Abstract As the manufacturing sector increasingly emphasizes production efficiency and product quality, predicting cutting tool remaining useful life (RUL) and conducting failure analysis become essential for ensuring continuity and reducing costs. However, the uncertainties in the tool wear process complicate accurate predictions and decision-making. This paper introduces a predictive maintenance decision-making approach using an ensemble model of convolutional neural network and bidirectional long short-term memory quantile regression (CNN-BiLSTMQR), enhanced by an attention mechanism and kernel density estimation (KDE). Firstly, signals related to tool wear are collected from various sensors, and a convolutional neural network (CNN) captures spatial features and local patterns in the data. These features are then input into a bidirectional long short-term memory (BiLSTM) network, which integrates a temporal attention mechanism and quantile regression (QR) to predict RULs at multiple quantiles. Subsequently, KDE generates the probability density distribution of the tool RULs. Finally, the costs and time associated with maintenance decisions are assessed to derive cost functions for tool replacement and ordering. By minimizing the two functions, the optimal timing for tool replacement and spare parts procurement is identified. Validation with a publicly available tool wear prediction dataset demonstrates that the proposed method effectively reduces maintenance costs while ensuring the safe and reliable operation of tools.

Technological evaluation of stones from the eastern region of the state of São Paulo, Brazil, for railway ballast

The correct choice of a stone aggregate for railway ballast is directly related to the stability, safety, efficiency, and maintenance costs of the track. The aggregate must meet several criteria to ensure it is the most appropriate material. Thus, the present study aimed to evaluate four distinct stones: two granites, a diabase, and a basalt, all mined in the eastern region of the state of São Paulo, Brazil, regarding their applicability as ballast. Using Brazilian and ASTM procedures for stone samples, the tests were conducted on 55 and 75-mm specimens, in dry and wet conditions. As a result, all stones met the physical properties established by the standards. Among the mechanical properties, uniaxial compressive strength stood out, with all stones where the tested materials advantageously exceeding the 100 MPa required by the Brazilian standard. Regarding the accelerated weathering test with ethylene glycol, only basalt showed more significant changes, although its resistance to weathering was still lower than 10%. Our findings indicate that these stones perform adequately as ballast for railway applications. The study is expected to contribute to revisions of technical standards and the improvement of a database on Brazilian stone materials for use as railway ballast.

Experimental Validation of Virtual Torque Sensing for Wind Turbine Gearboxes Based on Strain Measurements

ABSTRACTIn efforts to reduce the operation and maintenance cost of wind turbines, there is an increasing interest to monitor key turbine quantities such as the torque load on the gearbox. Monitoring the torque paves the way for the calculation of remaining useful lifetime, leading to cost reductions through improved reliability and maintenance planning. In order to avoid expensive direct torque sensors, this paper investigates the potential of virtual torque sensing, a technique based on 3 basic components: first, a set of non‐intrusive sensors installed on the gearbox. Three groups of strain gauges on the gearbox as well an angular encoder are considered in this paper. Second is a physics‐based model, capable of predicting the response of aforementioned sensors. These models are constructed with a purposeful balance between accuracy and computational cost. Model validation and updating are performed to ensure efficient and accurate prediction of the sensor output. Finally, an augmented extended Kalman filter (AEKF) is used to combine the measured response with predictions from the model to infer the gearbox input torque. Since a key factor determining the performance of the AEKF is the tuning of the AEKF covariance matrices, multiple methods are introduced to systematically tune the covariance matrices. Experimental validation results show that the virtual torque sensor can detect the load torque with a normalized mean absolute error (NMAE) between 3.41% and 7.47%, depending on the sensor set. The influence of the amount of sensors used and the tuning method are also investigated.

Implementasi Software-Defined Network Terintegrasi Firewall pada Proxmox untuk Pengontrolan Konfigurasi Jaringan dan Pengamanan Layanan Container

Virtualization technology has helped companies consolidate various server roles into a single physical server, reducing hardware costs. Hypervisor is a software in virtualization that is used to manage server hardware, allowing multiple Virtual Machines (VM)/Containers (CT) to run on a single physical machine. Companies face various challenges to remain competitive in the digital era, such as the need for rapid deployment of virtual guests and virtual networks on hypervisors in development, testing, and production environments, as well as securing network services. The purpose of this study is to implement SDN on hypervisors to centrally control virtual network configurations with a simple design, reducing setup and maintenance costs and time. In addition, it also implements a firewall and Virtual Private Network (VPN) based on OpenVPN and a reverse proxy to secure the hypervisor and VM/CT so that services remain available. This study presents a new approach that integrates Software-Defined Network (SDN)-based network management with comprehensive security solutions on hypervisors. This approach combines efficiency in network management and security that have rarely been focused on simultaneously in previous studies. The research method uses the Network Development Life Cycle (NDLC). The hypervisor used is Proxmox Virtual Environment (PVE) which is installed on the Virtual Private Server (VPS) provider IDCloudHost. Based on the results of the trials that have been carried out, it can be concluded that the simple zone type SDN on PVE can be used to control network configurations centrally and more simply such as routing, Dynamic Host Configuration Protocol (DHCP), Source Network Address Translation (SNAT), hostname registration and Internet Protocol (IP) from CT to forward lookup zone on the Domain Name System (DNS) server. Activating the firewall and creating rules at the cluster and CT levels from PVE and OpenVPN can protect the infrastructure when accessed both internally and externally. While the implementation of nginx reverse proxy can secure access to HTTP/HTTPS services on CT in PVE.

DESIGN OF LOW VOLUME CONCRETE ROADS

This article by Robert G. Huber explores the evolution and benefits of low volume concrete roads, focusing on the pioneering use of the slipform paving method in Iowa. It traces the origins back to 1946 when James W. Johnson conceptualized a machine that laid concrete without the need for side forms. Successive developments led to the creation of more advanced machines, with the first practical applications on county highways by the late 1940s. By the 1950s, the method had spread beyond Iowa, as other states and equipment manufacturers recognized the efficiency and cost-effectiveness of slipform paving. Notably, the slipform paver underwent significant improvements, facilitating its adoption for wider use. The paper details the specific adjustments in design and operation made in Iowa, such as the adoption of Type B concrete with varying cement content suitable for local conditions, and adjustments to subgrade preparation to enhance durability and performance. It also discusses the economic benefits, highlighting significant cost savings over traditional paving methods, alongside the reduced maintenance costs observed in Washington County. Furthermore, Huber elaborates on the broader adoption of slipform paving, predicting its dominance in the paving industry. The narrative is supported by data on road paving activities and costs over the years, providing a comprehensive view of the method’s impact on road construction in Iowa. This historical and technical account not only underscores the innovation's practicality but also its role in advancing road construction technology, making a case for its continued use and development. (Abstract generated by AI tool ChatGPT 4)

Self‐healing and in situ real‐time damage detection of stress‐responsive core‐shell microcapsules polymer composites

AbstractSelf‐healing technology based on micro/nano‐encapsulated materials has shown great potential in extending material lifespan, preventing performance degradation, and reducing maintenance costs. In this study, a novel mechanically responsive microcapsule is successfully prepared by in‐situ polymerization, which can be used for self‐healing of polymer composites. The core‐shell microstructure is characterized, and its stability and potential self‐healing ability is verified. The microcapsule is introduced into polymer matrix, and the epoxy fingerprint region at the preseted defect location is monitored in real‐time under stress stimulus using infrared spectroscopy, revealing the process from microcapsules rupture to liquid core materials releasing and self‐healing mechanism. Tensile tests are conducted to explore the effect of microcapsule fillers on the mechanical properties of polymer composites. The optimal microcapsule content is determined based on the comprehensive evaluations of self‐healing performance. The core‐shell microcapsules provide excellent in‐situ real‐time self‐healing capability for composite matrix damage during load‐bearing. The self‐healing under stress stimulus can better guarantee the reliability of polymer composites in service.Highlights A novel stress responsive microcapsule is prepared for polymer self‐healing. Stability and self‐healing ability of core‐shell microcapsule is verified. Preseted defect is in situ real‐time detected to reveal self‐healing mechanism. Effect of microcapsule content on composite mechanical properties is explored. Self‐healing better guarantee the reliability of polymer composites in service.

RAM-based Data Analytics for Power Plant Case Study: Steam Power Plant in Thailand

This research project aims to improve a power plant maintenance program by using the theory of RAM (Reliability Availability and Maintainability). A North Bangkok Combined Cycle Power Plant, especially a steam turbines power plant, is selected to conduct research from 2021 to 2022. The steps of this research can be separated into 2 phases as follows; the first phase involves root cause analysis from failure notifications stored in the CMMS-KKS code database and risk prioritization. Unit C10 and the compressed air unit system in steam turbines operation are the critical systems subsequently, the RAM approach is applied to improve a preventive maintenance program of unit C10 by estimating an operation time before failure, system availability, and the ability to repair. RAM information is brought to reschedule a PM program, for example, MTTF, MTTR, failure rate, and repair rate, etc. It is found that the percentage of unit C10 system reliability, R(t), and availability, A(t), are higher than 90%. Furthermore, the trade-off between the cost of maintenance and failure for unit C10 is decided by running unit C10 at a percentage of reliability of 88% which can schedule a maintenance interval every 300 hours