System Maintenance Research Articles

Terminal pose control method of power tower climbing robot for job safety

Abstract With the increasing demand for power system maintenance, the safety and efficiency of power tower climbing operations have become the focus of industry attention. This article proposes an end pose control method for power tower climbing robots aimed at job safety, targeting the problem of end pose control for power tower climbing robots. Firstly, the motion data of the power tower climbing robots is collected by deploying sensors. Subsequently, preprocessing and feature extraction are performed on the collected data to ensure its accuracy and availability. On this basis, an accurate end pose control model for climbing robots is constructed, which can adjust the robot’s motion parameters in real time to adapt to the complex changes in the tower structure. The experimental results show that the control method is effective and stable in actual climbing operations, significantly improving the efficiency and safety of climbing robots. This provides an innovative solution for the automation maintenance of power towers, which has important theoretical value and practical application prospects.

Migración de serverbox hacia un sistema de microservicios aplicando arquitectura vertical

Efficient data management is essential in public administration for the effective operation of municipal cadastral systems. In 2010, the Multifinality Cadastral Modernization Program was implemented, a partnership between the Puebla municipality government and Sistemas de Información Geográfica S.A. de C.V. (SIGSA), which included the ServerBox platform. Over time, the monolithic architecture of ServerBox showed limitations in maintenance, updates, and change deployment. This project hypothesizes that migrating ServerBox to a distributed microservices architecture, using a vertical approach, can overcome these challenges and improve operational areas of the system. The proposed design establishes a modular and scalable structure that facilitates system maintenance and evolution. The implementation includes the development of TagHelpers in ASP.NET Core to simplify HTML element creation and the use of snippets to streamline coding. Additionally, a JSON configuration file is defined to manage multiple deployment environments. This strategic approach to migrating ServerBox to a distributed and vertical architecture offers significant advantages in terms of scalability, maintenance, and software development efficiency, and can serve as a guide for similar projects in public administration.

Development of a Machine-learning-based Framework to Assess the Performance of Water Distribution Systems

Recent statistics indicate that the national water supply coverage is approximately 99%, reflecting a high level of service. However, the aging infrastructure continues to cause issues such as pipe failures and water quality concerns, creating operational difficulties. To solve these issues, performance assessments are used to quantitatively evaluate the system conditions and ensure effective maintenance. In Korea, these assessments are based on a scoring system that combines direct and indirect assessments. However, direct assessment of all pipes is limited by cost and time constraints. To address this issue, a deep neural network is used to assess the aging of water distribution systems. This study develops a framework to estimate direct results from indirect data by analyzing the correlation between indirect and direct assessments. Additionally, data augmentation is considered to compensate for the lack of training data in direct assessments, improve the reliability of performance assessments, and support better prioritization in system maintenance.

Caregivers in implantable brain-computer interface research: a scoping review.

While the ethical significance of caregivers in neurological research has increasingly been recognized, the role of caregivers in brain-computer interface (BCI) research has received relatively less attention. This report investigates the extent to which caregivers are mentioned in publications describing implantable BCI (iBCI) research for individuals with motor dysfunction, communication impairment, and blindness. The scoping review was conducted in June 2024 using the PubMed and Web of Science bibliographic databases. The articles were systematically searched using query terms for caregivers, family members, and guardians, and the results were quantitatively and qualitatively analyzed. Our search yielded 315 unique studies, 78 of which were included in this scoping review. Thirty-four (43.6%) of the 78 articles mentioned the study participant's caregivers. We sorted these into 5 categories: Twenty-two (64.7%) of the 34 articles thanked caregivers in the acknowledgement section, 6 (17.6%) articles described the caregiver's role with regard to the consent process, 12 (35.3%) described the caregiver's role in the technical maintenance and upkeep of the BCI system or in other procedural aspects of the study, 9 (26.5%) discussed how the BCI enhanced participant communication and goal-directed behavior with the help of a caregiver, and 3 (8.8%) articles included general comments that did not fit into the other categories but still related to the importance of caregivers in the lives of the research participants. Caregivers were mentioned in less than half of BCI studies in this review. The studies that offered more robust discussions of caregivers provide valuable insight into the integral role that caregivers play in supporting the study participants and the research process. Attention to the role of caregivers in successful BCI research studies can help guide the responsible development of future BCI study protocols.

Review of Domestic and Overseas Regulations and Technical Standards for Water Mist Fire Extinguishing Systems

The water mist system is an eco-friendly fire-extinguishing technology that suppresses fires using fine droplets with diameters of several hundred micrometers. This suppression occurs through a combination of smothering and cooling effects, as well as by blocking radiant heat. Compared to traditional water-based fire-extinguishing systems, water mist systems provide similar fire suppression capabilities while using only 10% of the water. Internationally, various organizations, such as the International Maritime Organization, Underwriters Laboratories, and Factory Mutual, have established standards and technical guidelines for water mist systems, outlining fire-testing methods and certification procedures. However, domestically, although standards and technical guidelines for water mist systems exist, specific requirements and procedures for certifying the performance of different types of protected objects have not yet been established. This gap has resulted in challenges across diverse operational environments where water mist systems are implemented, primarily due to the lack of clarity in technical standards. This study aims to compare and review the current regulations and standards for the design, manufacture, installation, certification, and maintenance of water mist systems, both domestically and internationally. It proposes a reasonable operational plan to broaden the applications of water mist systems in our country and contribute to the establishment of systematic and stable regulatory frameworks for their future use.

An Analysis of Visitor Behaviors and Satisfaction in Urban Forests: Focused on Major Forests in Incheon

Background and objective: Urban forests have increasingly integrated recreational and welfare facilities to enhance the quality of life for urban residents. This study explores the behaviors, perceptions, and satisfaction of visitors to major forests in Incheon Metropolitan City, aiming to offer guidelines for the improvement of recreational facilities within these urban forests. By understanding visitor needs and preferences, the study seeks to contribute to the development of more user-friendly and sustainable forest environments that cater to the well-being of city dwellers.Methods: The study focused on five major forests in Incheon: Munhaksan Mountain, Cheongnyangsan Mountain, Manwolsan Mountain, Wonjeoksan Mountain, and Gyeyangsan Mountain. Data were collected through field surveys and analyzed using cross-tabulation, one-way ANOVA, and regression analysis. Based on the results, targeted recommendations were made for the enhancement of recreational facilities in each forest.Results: For Munhaksan Mountain, expanding recreational spaces and offering trail-specific programs are key priorities, along with developing a maintenance plan to improve visitor satisfaction. In Cheongnyangsan Mountain, expanding popular facilities like barefoot walking paths and optimizing the forest structure for air purification and noise reduction are needed. Manwolsan Mountain requires a more efficient maintenance system, focusing on security, amenities, and visitor services to address the issue of low satisfaction. Wonjeoksan Mountain should enhance trail safety with more CCTVs and improve amenities such as restrooms and trash disposal. Gyeyangsan Mountain needs more rest areas and amenities, addressing congestion in the northwest while preserving natural scenery and providing an information platform for younger visitors.Conclusion: This study contributes to optimizing the use of urban forests and improving the quality of life for residents by proposing a strategic framework for the development of recreational welfare in forests. The findings are expected to serve as a valuable resource for local governments in formulating rational forest management and usage plans.

Cost-Effective Dust Detection on Solar PV Panels through Deep Learning: A Step towards Automated Maintenance Systems

Abstract: Accumulation of dust on solar panels impacts the overall efficiency and the amount of energy it produces. Detecting and mitigating dust accumulation is therefore crucial for optimizing solar energy production. While various techniques exist for detecting dust to schedule cleaning, many of these methods uses licensed software like MATLAB, which can be financially burdensome. This study proposes the use of modified pre-trained ResNet-50 model architecture with an adjusted fully connected layer for binary classification. An experimental setup was installed utilizing a single 75 Wp panel with an inclination maintained at 30-degree angle. The fine dirt particles were artificially introduced and datasets of images of clean and dusty panels were collected from five different sides were taken, to compensate for the surface reflectance from the PV panel due to camera angles. Those datasets were used to train and test the model and the accuracy achieved was 90%. The model's ability to detect dust with minimal false positives ensures more efficient maintenance scheduling. This research demonstrates the potential of AI-driven dust detection systems to enhance the operational efficiency of solar PV installations.

Effluents from septic systems and impact on groundwater contamination: a systematic review.

Globally, 2.2 billion people rely on groundwater for their water supply, and 2.8 billion use onsite sanitation systems for their sanitation needs. Groundwater contamination from septic systems is a critical public health concern, linked with diseases related to water sanitation and hygiene. Despite the severe impacts of septic systems on groundwater quality, comprehensive global studies remain limited. This study conducted a systematic review of articles published between 2012 and 2023 on topics related to septic systems and groundwater contamination, and 82 peer-reviewed articles met the inclusion criteria. The review identified key contaminants, including E.coli, nitrate, Enterococcus spp., total coliform, ammonium, phosphate, chlorides, and pharmaceuticals. Research on microbial indicators is more prevalent in Africa, while research on nutrients is common in North America. Research on organic contaminants including polyfluoroalkyl substances (PFAS), pharmaceuticals, and personal care products (PCPPs) is limited, particularly in low-and middle-income countries. Critical factors contributing to groundwater contamination include soil, hydrogeological conditions, climate, septic system maintenance and functioning, and septic density. The goal of this study was to comprehensively assess the extent of groundwater contamination resulting from septic system effluents by identifying the major contaminants typically found in affected groundwater sources and exploring the factors contributing to contamination. Identifying the major contaminants and factors related to groundwater contamination from septic systems is crucial for developing effective management strategies to protect groundwater sources.

Aquaporin channels in desalination: Mechanical properties and operational load analysis

The scarcity of freshwater sources and the global demand for drinking water have spurred researchers worldwide to develop new and efficient desalination and water purification technologies. One promising method is desalination using aquaporin (AQP) channels, which are highly regarded for their biocompatibility and exceptional desalination efficiency. However, there is limited information on the mechanical behavior of these proteins under operational loads and how they maintain their function and resilience over time. This research employs all-atom molecular dynamics simulation to calculate the mechanical properties of the channels at the nanoscale. It also uses the finite element method to analyze the behavior of channels in vesicles embedded in composite plates at the macroscale under operational loads and conditions. Our research shows that the force on vesicle walls changes considerably with applied pressure, peaking at 14 pN at 55 bar. This variability highlights the need to carefully assess the weakest parts of the nanochannels, especially the helices HB and H1, which are susceptible to high strain and possible unfolding under extended stress. The results indicate the force tolerance threshold of the subsystems, guiding the application of appropriate force conditions for optimal performance and long-term system maintenance. Beyond desalination systems, the findings offer useful information for researchers working with aquaporin nanochannels in applications such as targeted drug release systems based on protein nanovalves, solid-state sequencing systems, and more.

AI-driven predictive maintenance and optimization of renewable energy systems for enhanced operational efficiency and longevity

The rapid growth of renewable energy systems necessitates advanced strategies for maintenance and optimization to ensure long-term operational efficiency and sustainability. Traditional approaches often fall short in predicting failures and optimizing performance across diverse and dynamic renewable energy infrastructures. This study investigates the application of artificial intelligence (AI) techniques for predictive maintenance and optimization of renewable energy systems, with the aim of enhancing operational efficiency and extending system longevity. We employ a combination of machine learning algorithms, including deep neural networks and reinforcement learning, to develop predictive models and optimization strategies. These models are trained on large-scale datasets collected from operational wind farms, solar installations, and hydroelectric plants. Our results demonstrate that AI-driven approaches can predict equipment failures with 92% accuracy, reducing unplanned downtime by 35% compared to traditional methods. Moreover, AI-optimized operational parameters improved overall energy output by 8.5% across the studied systems. The proposed framework also showed adaptability to various environmental conditions and system configurations, suggesting broad applicability across the renewable energy sector. This research underscores the significant potential of AI in revolutionizing maintenance practices and operational strategies in renewable energy systems, paving the way for more reliable, efficient, and sustainable clean energy production.

Development of a Hybrid AI Model for Fault Prediction in Rod Pumping System for Petroleum Well Production

Rod pumping systems are widely used in oil wells. Accurate fault prediction could reduce equipment fault rate and has practical significance in improving oilfield production efficiency. This paper analyzed the production journal of rod pumping wells in block X of Xinjiang Oilfield. According to the production journal, oil well maintenance operations are primarily caused by five types of faults: scale, wax, corrosion, fatigue, and wear. These faults make up approximately 90% of all faults. 1354 oil wells in the block that experienced workover operations as a result of the aforementioned factors were chosen as the research objects for this paper. To lower the percentage of data noise, wavelet threshold denoising and variational mode decomposition were used. Based on the bidirectional long short-term memory network, an intelligent model for fault prediction was built. It was trained and verified with the help of the sparrow search algorithm. Its efficacy was demonstrated by testing various deep learning models in the same setting and with identical parameters. The results show that the prediction accuracy of the model is the highest compared with other 11 models, reaching 98.61%. It is suggested that the model using artificial intelligence can provide an accurate fault warning for the oilfield and offer guidance for the maintenance of the rod pumping system, which is meant to reduce the occurrence of production stagnation and resource waste.

Impact of biogas adoption on household energy use and livelihood improvement in Kenya: an overview on a roadmap toward sustainability

Purpose This study assesses the impact of biogas adoption on household energy use and livelihood improvement. Also, this study aims to clarify the benefits of biogas adoption as a pathway to sustainable household energy. Design/methodology/approach The study explored the benefits of adopting biogas uptake. Fourteen sustainability indicators were identified, validated and categorized. Findings Adopting biogas technology provides numerous benefits, including better air quality, reduced deforestation and lower greenhouse gas emissions. Households can replace up to 4.5 tons of wood fuel, cutting CO2 emissions by around 6.75 tons annually. This shift saves approximately US$25 (Ksh.3223) monthly and frees up 45.5 h per week, enabling families to pursue additional income-generating activities. Biogas also produces digestate, a natural fertilizer that improves soil fertility, water retention and reduces erosion while minimizing the need for chemical fertilizers. Integrating biogas in livestock housing reduces odors, pathogens and methane emissions. Research limitations/implications The findings demonstrate numerous social, economic and environmental implications of biogas use. Social implications Health benefits include lower exposure to smoke and particulate matter, particularly benefiting women and children by reducing respiratory issues, improving lighting and enhancing educational opportunities. Biogas further improves hygiene, promotes cleanliness, strengthens energy security and alleviates energy poverty. In addition, the construction, operation and maintenance of biogas systems create jobs, and the use of digestate enhances agricultural productivity. Originality/value This study provides a unique and thorough analysis of the benefits of biogas, offering valuable insights and outlining a sustainable approach.

Architecting Resilience : A Framework for Secure and Compliant Healthcare IT Infrastructures

Healthcare information technology (IT) systems face unique challenges in maintaining data security and regulatory compliance while supporting critical patient care functions. This article provides a comprehensive analysis of the complex interplay between data protection measures and compliance requirements in the healthcare sector. We examine key components of data security, including encryption, data integrity measures, and secure transfer protocols, with a specific focus on their application to sensitive patient information. The impact of regulations such as HIPAA on system design and maintenance is explored, offering insights into the alignment of IT practices with evolving standards. Through a review of current literature and industry best practices, we present strategies for risk management, employee training, and the implementation of technical controls that address both security and compliance needs. Emerging trends, including cloud computing, the Internet of Medical Things (IoMT), and artificial intelligence in healthcare security, are discussed to provide a forward-looking perspective. This article contributes to the ongoing dialogue on balancing innovation with security in healthcare IT, offering practical recommendations for healthcare organizations to enhance their data protection measures while ensuring regulatory compliance.

Advantages of Big Data Analysis and AI Technology in Data Collecting And Processing of Preventive Maintenance

In recent years, the emergence of Cyber-Physical systems (CPS), big data, cloud computing, and industrial wireless networks have promoted the development of Industry 4.0. Intelligent manufacturing systems are the leaders of this change. In smart manufacturing, the emphasis will be placed on using technologies such as big data analytics, cloud computing, edge computing, and artificial intelligence (AI). The advantages of new technologies are significantly reflected in the comparison of preventive maintenance and traditional maintenance based on intelligent technology: preventive maintenance significantly reduces the cost of prevention and, at the same time, dramatically improves the accuracy and timeliness of maintenance, bringing inestimable value to intelligent manufacturing. This paper will analyze new technologies’ innovative advantages and technical prospects through big data analysis and specific application scenarios of AI technology in preventive maintenance. Firstly, in data acquisition, this paper examines the architecture of preventive maintenance systems based on big data. Then, it analyzes the composition of the data pipeline: in the realization of data collection and processing in the field of cloud computing and edge computing, and the aspect of data processing, it discusses the participation of AI technology in data understanding and processing and the unprecedented changes it brings.

Sources and determinants of exposure to indoor air pollutants in schools and hospitals: a review

Abstract Background Indoor Air Quality (IAQ) is an important determinant of health. This study aimed to review the available scientific evidence about indoor pollutants and determinants of exposure in several settings, in the scope of the K-HEALTHinAIR project. Methods The systematic review was performed for the sources, concentrations, and determinants of pollutants in schools in the European region for 2013-2023. Articles were selected for data extraction if they were epidemiological studies, reported primary data for indoor air and pollutant levels, and, identified sources and determinants of exposure. Results The main pollutants affecting IAQ in schools and hospitals were carbon dioxide, volatile organic compounds (e.g., formaldehyde, terpenes), particulate matter, nitrogen dioxide, radon, ozone, and carbon monoxide. Besides the outdoor environment, several important indoor sources of emissions were identified such as occupancy, resuspension, cleaning products. Ventilation was the main determinant influencing the indoor levels of pollutants, stressing the importance of good planning, installation, and maintenance of mechanical ventilation systems in buildings. Other determinants were the number of occupants and their activities, and the building characteristics (e.g., type of floor, location, etc). Conclusions Results revealed indoor air quality as a public health concern since several pollutants are present in indoor settings. The identification of sources and determinants of exposure is fundamental for determining preventive measures in a public health perspective. Acknowledgments: K-HEALTHinAIR team for screening and data extraction: Mariana Corda, Jose Fermoso, Alicia Aguado, Sandra Rodriguez, Hanns Moshammer, Mireia Ferri, Belén Costa, Leticia Pérez, Wojciech Hanke, Artur Badyda, Piotr Kepa, Katarzyna Affek, Nina Doskocz, Laura Martín, Oguz Mulayim, Cesar Mediavilla Martinez, Alba Gómez, Ruben González, Isaac Cano, Josep Roca, Simon de Leede.

Perawatan Preventive Maintenance Mobil Satlantas Model Sedan Mazda dengan Metode Reliability Centered Maintenance pada Polres Kota X

The aim of this research is to find out the preventive maintenance of Mazda Sedan Model Traffic Cars using the reliability centered maintenance method at the City functional block diagram, determining system function and functional failure, failure mode and effect analysis, logic tree analysis, and task selection. The results of this research are that the maintenance system for traffic vehicle units carries out routine check-ups every 5,000 kilometers or every 3 months and for brake and tire components every 10,000 kilometers or 6 months, the damage that appears with the current maintenance system is the break caliper (right front), hose (water pump), and ball joint, factors that arise with current maintenance problems are safety problems, proposed improvements that need to be made to resolve the problem are preventive maintenance for the break caliper (right front), hose (water pump), and ball joint every 11000 hours or 458 days.

Leveraging the Synergy of Digital Twins and Artificial Intelligence for Sustainable Power Grids: A Scoping Review

As outlined by the International Energy Agency, 44% of carbon emissions in 2021 were attributed to electricity and heat generation. Under this critical scenario, the power industry has adopted technologies promoting sustainability in the form of smart grids, microgrids, and renewable energy. To overcome the technical challenges associated with these emerging approaches and to preserve the stability and reliability of the power system, integrating advanced digital technologies such as Digital Twins (DTs) and Artificial Intelligence (AI) is crucial. While existing research has explored DTs and AI in power systems separately, an overarching review of their combined, synergetic application in sustainable power systems is lacking. Hence, in this work, a comprehensive scoping review is conducted under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). The main results of this review analysed the breadth and relationships among power systems, DTs, and AI dynamics and presented an evolutionary timeline with three distinct periods of maturity. The prominent utilisation of deep learning, supervised learning, reinforcement learning, and swarm intelligence techniques was identified as mainly constrained to power system operations and maintenance functions, along with the potential for more sophisticated AI techniques in computer vision, natural language processing, and smart robotics. This review also discovered sustainability-related objectives addressed by AI-powered DTs in power systems, encompassing renewable energy integration and energy efficiency, while encouraging the investigation of more direct efforts on sustainable power systems.

Corporate Governance Practices of Changampally Ayurveda Vaidyasala - A Study from Employees’ Perspective

Corporate governance is the arrangement in which companies are directed and controlled (OECD, 2004). Each stakeholder is important to every company and Corporate Governance is the driver of stakeholders' satisfaction (Rejeb, 2012). Among the stakeholders of a company, employees are the most critical stakeholders as they hold direct and long-term contact with the productivity of every company (Lynch Fannon, 2006). This study intended to understand employees' perceptions of Changampally Ayurveda Vaidyasala's corporate governance practices. The famous Changampally family was invited from Tulunadu to perform in the famous Mamanka festival in Thirunavaya, and later, they were forced to settle in Thirunavaya. (http://www.changampallyayurveda.com/ayurveda-medicine-manufacturing.php, n.d.). They are considered one of the oldest and most renowned Ayurveda practitioners specializing in AsthiMarma Chikilsa. It is an Ayurvedic manufacturing company with a GMP certification and is well-known for its employee maintenance system. This study went through their existing corporate governance practices like transparency, fairness, accountability, and responsibility of the company from the employees’ perspective. It is found that the company hasfavourable transparency, fairness, accountability, and responsibility policies towards employees and itsoverall corporate governance practices are found exemplary.

Autophagy and proteasomes in thymic epithelial cells: essential bulk protein degradation systems for immune homeostasis maintenance.

The thymus is a central organ that controls T cell development. Thymic epithelial cells (TECs) create a unique microenvironment essential for the differentiation of major histocompatibility complex (MHC)-restricted and self-tolerant T cells. TECs present a complex of self-peptides and MHC molecules (self-pMHCs) to immature T cells and regulate their survival and differentiation based on their affinity for self-pMHCs. The processing of self-peptides in TECs depends on bulk protein degradation systems, specifically autophagy and proteasomes. Studies using autophagy- and proteasome-deficient mouse models have demonstrated that these degradation systems in TECs are indispensable for maintaining immune homeostasis. Although autophagy and proteasomes are ubiquitous in nearly all eukaryotic cells, TECs exhibit unique characteristics in their autophagy and proteasome functions. Autophagy in TECs is constitutively active and independent of stress responses, while TEC proteasomes contain specialized catalytic subunits. This review summarizes the distinctive characteristics of autophagy and proteasomes in TECs and their roles in immune system regulation.

NexoNet: Blockchain Online Social Media with User-Centric Multiple Incentive Mechanism and PoAP Consensus Mechanism

Online social media (OSM) has revolutionized the manner in which communication unfolds, enabling users to spontaneously generate, disseminate, share, and aggregate multimedia data across the internet. Nevertheless, in this exchange of information, OSM platforms assume a dominant, central role, wielding excessive power. Blockchain online social media (BOSM) seeks to mitigate the drawbacks of traditional centralized OSM by leveraging the decentralized nature of blockchain technology, migrating the functionalities of social media into a decentralized realm, and positioning the users at the core of the OSM ecosystem. However, current BOSM models often rely on tokens for incentives and are hampered by the centralized, inefficient blockchain consensus mechanisms, alongside vulnerabilities such as collusion attacks. This paper introduces a novel blockchain system, NexoNet, tailored for decentralized social media, exploring the application of blockchain technology in the realm of online social media from both technical and economic perspectives. The NexoNet quantifies and evaluates user participation within the system, employing a multiple incentive mechanism to equitably distribute value created by users without the need for tokens. Furthermore, we propose the Proof-of-Active-Participation (PoAP) blockchain consensus mechanism, enabling all users to partake in the maintenance of the blockchain system, thus ensuring its security and efficiency. Theoretical analysis and simulations across various scenarios demonstrate that the NexoNet, with extensive user engagement, achieves equitable value distribution through its multiple incentive mechanism. It successfully safeguards against a spectrum of malicious attacks and attains high transaction processing efficiency. The simulation results show that NexoNet achieves an average transaction throughput of 2000 transactions per second (TPS) and a consensus delay of 2.385 s with 100 maintainers in the network. Furthermore, our tests demonstrated that even collusion with users comprising 75% of the total would only allow an additional 30 chances to propose a block. By deeply integrating user behavior with the underlying mechanisms of the blockchain system, the NexoNet fosters a user-centric blockchain social media ecosystem.