System Maintenance Research Articles

AI-enhanced fault-tolerant control and security in transportation and logistics systems: addressing physical and cyber threats

Transportation and logistics systems are becoming increasingly complex and critical to modern infrastructure. This paper proposes a novel AI-enhanced fault-tolerant control framework to address the dual challenges of physical malfunctions and cyber threats. By leveraging advanced machine learning algorithms and real-time data analytics, the proposed methodology aims to enhance the reliability, safety, and security of transportation and logistics systems. This research explores the foundations and practical implementations of AI-driven anomaly detection, predictive maintenance, and autonomous response systems. The findings demonstrate significant improvements in system resilience and robustness, making a substantial contribution to the field of intelligent transportation management.

АВТОМАТИЗАЦИЯ ПРОГНОЗИРОВАНИЯ НЕИСПРАВНОСТЕЙ МЕДТЕХНИКИ

The study identifies the problems faced by modern medical institutions, in particular, the Meditsina clinic. The management, maintenance, and user operation of medical devices cause these difficulties. To overcome them, it is necessary to revise the system of maintenance and repair of medical equipment, as well as to ensure continuous professional development of the employees responsible for the equipment operation. The paper proposes an information model that includes technical tools for forecasting, such as exponential smoothing. The authors create software implementa-tion of the information system and forecasting module based on the research; test these developments in the working conditions of the medical and preventive institution Meditsina. Thanks to the constructed forecast of the service life of the PHoX Plus analyzer parts, it is possible to adjust the purchase of consumables downwards.

Modular and Portable System Design for 3D Imaging of Breast Tumors Using Electrical Impedance Tomography

This paper presents a prototype of a portable and modular electrical impedance tomography (EIT) system for breast tumor detection. The proposed system uses MATLAB to generate three-dimensional representations of breast tissue. The modular architecture of the system allows for flexible customization and scalability. It consists of several interconnected modules. Each module can be easily replaced or upgraded, facilitating system maintenance and future enhancements. Testing of the prototype has shown promising results in preliminary screening based on experimental studies. Agar models were used for the experimental stage of this project. The 3D representations provide clinicians with valuable information for accurate diagnosis and treatment planning. Further research and refinement of the system is warranted to validate its performance in future clinical trials.

Разработка комплексного показателя оценки состояния городского электротранспорта для органов исполнительной власти Российской Федерации

This article analyzes 115 cities on the territory of the Russian Federation in which one or more urban electric transport systems operate. It is determined that in the modern industry there is no single comprehensive indicator of the state of urban electric transport, which would allow an assessment of the state of systems not only in relation to their own indicators of previous years, but also in relation to other, similar systems. A comprehensive indicator is proposed that will allow a comprehensive assessment of the state of urban electric transport and interpret it in numerical terms for its application by state and regional authorities, allowing to quickly draw conclusions and make a decision on further adjustment of the functioning of the electric transport system, characterized by a relatively simple formula for calculation and does not require specific competencies for those conducting the analysis. The key indicators of urban electric transport are systematized, including those related to the operated energy infrastructure, linear infrastructure, park facilities, including technical buildings and structures for system maintenance, indicators related to rolling stock and its key technical characteristics, as well as the availability of urban electric transport for residents of the considered locality and passenger flows within this framework systems. The boundaries of the values of the final indicator have been determined, within which the state of urban electric transport will be assessed by state and regional authorities as satisfactory or unsatisfactory and requiring decisions on revising approaches to the development of the system within the framework of strategic transport planning documents.

Analysis of Plant Watering Efficiency Using IoT Technology Controlled Through Google Assistant

This paper presents a systematic literature review (SLR) of 120 academic documents sourced from the Scopus database, which analyses the efficiency of crop watering systems utilizing Internet of Things (IoT) technology controlled via Google Assistant. The review explores key advances in IoT-based irrigation systems, highlighting how real-time data from sensors and voice-controlled automation improve water efficiency and user experience. The findings reveal that IoT systems can reduce water wastage by 25-30%, optimise plant health, and offer convenience through voice commands. However, challenges such as connectivity issues, high implementation costs, and system maintenance complexity also need to be addressed. This paper discusses potential future research directions, including scalability, AI integration, and cost-effective solutions to expand the adoption of these technologies in agriculture and horticulture.

Enabling Precision Agriculture through a Web-Based Fertilization Management System for Nawungan Selopamioro Fruit Orchards

Precision Agriculture (PA) is an integrated farming system based on information and technology for managing agriculture to identify, analyze, and manage spatial and temporal diversity information in specific locations to obtain optimum and sustainable benefits while minimizing unwanted environmental impacts. Fertilization is one of the crucial phases in agricultural production process considering technical cultivation aspects, costs, and environmental impacts. The current fertilization process at Kebun Buah Nawungan Selopamioro (KBNS) is still conventional, so there is no standard rule in determining the fertilization dose. Therefore, a PA approach is needed to provide suitable fertilizer doses for agricultural production needs. This objective of this study was to develop of a web-based fertilizer management system, integrating with orchard management to enhance accessibility and decision-making. The system calculates fertilizer requirements by analyzing soil nutrient availability (N, P, K), cultivation area, crop type and age, and available fertilizer types. The development followed the waterfall methodology, encompassing stages from requirement analysis to system maintenance. The outcome is a web application that manages land assets, administrative activities, and fertilizer needs tailored to specific land blocks, crop characteristics, and nutrient inventories. Subsequent validation against field conditions ensures the accuracy of its recommendations. Although comprehensive testing confirmed a 100% success rate in functionality, the system currently operates within a limited scope of variables. Future enhancements are planned to incorporate broader agronomic factors, such as soil pH and texture, to augment the system's precision. Despite its limitations, this system represents a significant technological advance in precision agriculture, promising to improve fertilizer application efficiency and support sustainable farming practices.

Research on Maintenance Capability Standards for Drive Motor Systems of New Energy Vehicles

Developing industry recognized industry standards within the qualification framework is the benchmark for learning outcomes transformation. Based on the study of general competency standards, this article takes the maintenance competency requirements of new energy vehicle drive motor systems as a case study, and conducts a detailed analysis of the requirements for this module in vocational education undergraduate education for secondary and higher vocational schools. Finally, the vocational skill level standard for "electric vehicle high-voltage system evaluation and maintenance" is revised and preliminarily verified.

Patient-derived organoid model of olfactory ensheathing cell tumor.

We developed a culture model of a human olfactory ensheathing cell tumor. Cultured organoids resemble normal ensheathing cells. Assays suggest that this model provides a tool for studying the roles of these glial cells in the maintenance of the peripheral olfactory system.

WAYS TO IMPROVE THE EFFICIENCY OF TECHNOLOGICAL EQUIPMENT

Increasing the productivity of production systems and equipment in the meat industry is inextricably linked with maintenance activities. The article analyzes the influence of manufacturability, hygiene, reliability, simplicity of equipment design on the main indicators of the quality of meat products. It is shown that the Total Productive Maintenance (TRM) and Lean production strategy is an important tool for evaluating the Overall Equipment Effectiveness (OEE) indicator, which is used to evaluate the main types of losses that reduce the efficiency of equipment. The relationship of structural and mechanical characteristics of food masses, humidity of raw materials, temperature and viscosity, processing pressure, density on the optimal operating modes of the equipment is analyzed. Using the OEE concept, the time losses associated with the functioning of the equipment are determined. The types of losses are classified: downtime and changeover (availability or readiness) of equipment, short-term shutdown of equipment and reduction of processing speed (equipment performance), defective products and product losses at startup (quality) of equipment. The application of this method of studying the overall efficiency of the equipment can be applied to any technological equipment used in the meat industry and will eliminate many problems that arise during the operation of technological equipment, improve its technical and economic indicators, develop a system of measures to improve its maintenance and repair. Using this concept, it is possible to systematize the factors that reduce the efficiency of the equipment. At the same time, it is proved that information about the factors affecting the loss of equipment operation time allows you to create a system for maintenance and repair of equipment.

Muscle-resident mesenchymal progenitors sense and repair peripheral nerve injury via the GDNF-BDNF axis.

Fibro-adipogenic progenitors (FAPs) are muscle-resident mesenchymal progenitors that can contribute to muscle tissue homeostasis and regeneration, as well as postnatal maturation and lifelong maintenance of the neuromuscular system. Recently, traumatic injury to the peripheral nerve was shown to activate FAPs, suggesting that FAPs can respond to nerve injury. However, questions of how FAPs can sense the anatomically distant peripheral nerve injury and whether FAPs can directly contribute to nerve regeneration remained unanswered. Here, utilizing single-cell transcriptomics and mouse models, we discovered that a subset of FAPs expressing GDNF receptors Ret and Gfra1 can respond to peripheral nerve injury by sensing GDNF secreted by Schwann cells. Upon GDNF sensing, this subset becomes activated and expresses Bdnf. FAP-specific inactivation of Bdnf (Prrx1Cre; Bdnffl/fl) resulted in delayed nerve regeneration owing to defective remyelination, indicating that GDNF-sensing FAPs play an important role in the remyelination process during peripheral nerve regeneration. In aged mice, significantly reduced Bdnf expression in FAPs was observed upon nerve injury, suggesting the clinical relevance of FAP-derived BDNF in the age-related delays in nerve regeneration. Collectively, our study revealed the previously unidentified role of FAPs in peripheral nerve regeneration, and the molecular mechanism behind FAPs' response to peripheral nerve injury.

Non-inclusive g-extra diagnosability of interconnection networks under PMC model

System level fault diagnosis theory is of great weight in the design and maintenance of multiprocessor systems. In this paper, we propose a novel kind of diagnosability, called non-inclusive g-extra diagnosability, that requires all faulty sets to be non-inclusive and every connected fault-free subgraph has at least g+1 vertices. Furthermore, we determine the non-inclusive g-extra diagnosability of a class of graphs under PMC model. As applications, the non-inclusive g-extra diagnosability of (n,k)-star graph Sn,k, data center networks DCell (Dk,n), BCDC (Bn) and BCube (BCn,k) under PMC model are determined successively.

Forecasting models analysis for predictive maintenance

IntroductionThis study explores the shift toward predictive maintenance through real-time data analytics to minimize machine downtime and improve machinery insights in industrial environments. Predictive maintenance aims to enable proactive interventions by predicting failures, enhancing operational efficiency.MethodsThe research was conducted in three stages. First, BA Glass equipment was sensorized using OPC Router and PowerStudio SCADA to facilitate real-time data extraction. A predictive maintenance algorithm was then developed in Python to analyze sensor data, predict failures, and trigger alarms. Finally, various forecasting models, including Linear and Polynomial Regression, Simple and Double Exponential Smoothing, ARIMA, and Prophet, were evaluated using a combination of blocked cross-validation and rolling window methodologies. The algorithm calculated performance metrics such as MSE, RMSE, and MAE for different parameter configurations and training sizes.ResultsA comparative analysis between wired and wireless sensors concluded that wireless sensors, although more expensive, were more practical and interchangeable in the factory setting. The results from the evaluation of prediction models showed that the Double Exponential Smoothing (DES) model with an additive damped trend and linear models performed best for datasets with daily seasonality and gradual oscillations. For datasets with stable trends and higher frequency oscillations, ARIMA and Prophet models proved to be more accurate.DiscussionThese findings suggest that the choice of sensors and prediction models can significantly impact the effectiveness of predictive maintenance systems. Wireless sensors offer long-term benefits in terms of flexibility and practicality, while the DES model and ARIMA/Prophet models are optimal depending on the dataset characteristics. This research highlights the value of real-time data analytics and predictive models in industrial environments for reducing downtime and improving decision-making.

Revisiting Sentiment Analysis for Software Engineering in the Era of Large Language Models

Software development involves collaborative interactions where stakeholders express opinions across various platforms. Recognizing the sentiments conveyed in these interactions is crucial for the effective development and ongoing maintenance of software systems. For software products, analyzing the sentiment of user feedback, e.g., reviews, comments, and forum posts can provide valuable insights into user satisfaction and areas for improvement. This can guide the development of future updates and features. However, accurately identifying sentiments in software engineering datasets remains challenging. This study investigates bigger large language models (bLLMs) in addressing the labeled data shortage that hampers fine-tuned smaller large language models (sLLMs) in software engineering tasks. We conduct a comprehensive empirical study using five established datasets to assess three open-source bLLMs in zero-shot and few-shot scenarios. Additionally, we compare them with fine-tuned sLLMs, using sLLMs to learn contextual embeddings of text from software platforms. Our experimental findings demonstrate that bLLMs exhibit state-of-the-art performance on datasets marked by limited training data and imbalanced distributions. bLLMs can also achieve excellent performance under a zero-shot setting. However, when ample training data is available or the dataset exhibits a more balanced distribution, fine-tuned sLLMs can still achieve superior results.

Research on Convergence Media Ecological Model Based on Blockchain

Currently, the media industry is in the rapid development stage of media integration, which has brought about great changes in content production mode, presentation form, communication mechanism, operation and maintenance management, etc. At the same time, it is also faced with problems such as difficult information traceability, declining industry credibility, low data circulation quality and efficiency, difficult data security and user privacy protection, etc. Utilizing blockchain’s characteristics can solve these problems that the media industry is currently facing. This paper designs a convergence media ecology model based on blockchain (CMEM-BC), focusing on the basic elements of the model, node operation and maintenance system, node management mechanism, value circulation mechanism, and storage mechanism, trying to establish a decentralized, traceable, and immutable convergence media ecosystem. On this basis, this paper summarizes the ecological framework and ecological model of CMEM-BC. Finally, the paper describes the verification of the effectiveness of CMEM-BC in key links through simulation experiments, verifying that CMEM-BC has high originality and is more suitable for the application of convergence media ecology through model analysis and comparison.

Fault stands out in contrast: Zero-shot diagnosis method based on dual-level contrastive fusion network for control moment gyroscopes predictive maintenance

Control moment gyroscopes (CMGs) are the most common control actuators in spacecraft. Their predictive maintenance is crucial for on-orbit operations. However, due to the scarcity of CMG fault data, constructing a diagnosis system for predictive maintenance with CMGs poses significant challenges. Therefore, a zero-shot fault diagnosis method based on a dual-level contrastive learning fusion network was proposed. First, to address the difficulty in training CMG fault diagnosis models without fault data, a contrastive learning method based on CMG clusters was proposed to extract invariant features from healthy CMGs and achieve zero-shot diagnosis for predictive maintenance. Second, considering the limitations of information from a single sensor, a cross-sensor contrastive learning method was proposed to fuse features from different sensors. Third, to tackle the challenges of extracting weak potential fault features, a dual-level joint training method was introduced to enhance the model’s feature extraction capability. Finally, the proposed method was validated using real dataset collected from CMGs serviced on an in-orbit spacecraft. The results demonstrate that the method can achieve zero-shot fault diagnosis for control moment gyroscopes predictive maintenance. The code is available at https://github.com/IceLRiver/DCF.

Optimizing the Utilization of Online Public Access Catalog as a Means of Information Retrieval in Libraries

This study aims to explore the optimization of the Online Public Access Catalogue (OPAC) as an information retrieval tool in the Library of SMA Negeri 3 Medan, focusing on identifying the challenges and barriers to its effective use. A qualitative descriptive approach was employed, utilizing interviews, observations, and documentation as data collection methods. Interviews were conducted with a librarian and three students, while observations focused on the practical issues related to OPAC usage. The findings reveal that the use of OPAC in the library is not optimal, with challenges including system integration difficulties, insufficient librarian training, and limited user familiarity. Efforts to enhance OPAC usage involve promoting OPAC awareness, increasing computer access points, and ongoing system maintenance. However, the effectiveness of these measures is hindered by technical and educational constraints, highlighting the need for targeted interventions to fully leverage OPAC's potential in improving library services.

A comparative study of ensemble methods and multi-output classifiers for predictive maintenance of hydraulic systems

The maintenance and upkeep of hydraulic systems play a pivotal role in various industrial applications, from manufacturing processes to heavy machinery operations. Traditional strategies often face significant limitations, particularly concerning intervention time and the costs associated with delays between fault occurrence and maintenance. Over the past decades, proactive strategies have emerged with promising potential, primarily due to their predictive capabilities. These proactive approaches aim to anticipate faults and maintenance needs, thereby mitigating costs and operational disruptions associated with reactive approaches. This study explores the applicability of single output, ensemble methods, and the integration of multi-output classifiers in hydraulic predictive maintenance problems with relatively limited data. First, data is analyzed using Pearson correlation coefficients, and then feature extraction is conducted using recursive feature extraction, aiming to optimize the performance of predictive models, particularly Random Forest and CatBoost. Results show that the stacking ensemble method, incorporating LightGBM, XGBoost, CatBoost, and Random Forest, yields the most notable improvement, achieving a final accuracy of 98.63%. The results obtained in this study show satisfying performances for single-output models, ensemble methods, and multi-output models in predicting the health of hydraulic systems. Moreover, combining single output, ensemble methods, and the integration of multi-output classifiers has created a relatively reliable and robust predictive maintenance system.

Predictive Maintenance in IoT-Monitored Systems for Fault Prevention

This paper focuses on predictive maintenance for simple machinery systems monitored by the Internet of Things (IoT). As these systems can be challenging to model due to their complexity, diverse typologies, and limited operational lifespans, traditional predictive maintenance approaches face obstacles due to the lack of extensive historical data. To address this issue, we propose a novel clustering-based process that identifies potential machinery faults. The proposed approach lies in empowering decision-makers to define predictive maintenance policies based on the reliability of the proposed fault classification. Through a case study involving real sensor data from the doors of a transportation vehicle, specifically a bus, we demonstrate the practical applicability and effectiveness of our method in preemptively preventing faults and enhancing maintenance practices. By leveraging IoT sensor data and employing clustering techniques, our approach offers a promising avenue for cost-effective predictive maintenance strategies in simple machinery systems. As part of the quality assurance, a comparison between the predictive maintenance model for a simple machinery system, pattern recognition neural network, and support vector machine approaches has been conducted. For the last two methods, the performance is lower than the first one proposed.

Prediction and analysis of wheel flange wear on small curved track considering wheel-rail conformal and lubricated contact

Wheel flange wear on curved tracks is a significant issue that poses a threat to the safety of rail vehicles and leads to increased maintenance costs for railway operators. This study aims to develop a numerical model to predict wheel flange wear on small radius curves. The model integrates an efficient wheel-rail conformal contact model with laboratory-tested wear coefficients. By using virtual penetration and influence coefficients for conformal geometries, the model accurately simulates the conformal contact between the wheel flange and the rail gauge corner. The validity of the wheel flange wear prediction model is confirmed through field tracking testing. Using the proposed model, the effects of track layout parameters and solid lubricants on wheel flange wear are investigated. This study provides theoretical guidance for the operation and maintenance of the wheel-rail system to reduce wear.

Research on improved underwater cable image processing technique based on CNN-GAN

In this study, we propose a CNN-GAN-based real-time processing technique for filtering images of underwater cables used in power systems. This addresses the excessive interference impurities that are frequently observed in images captured by remotely operated vehicles (ROVs). The process begins with the input of the original image into the convolutional neural network (CNN). Subsequently, the training outcomes, which serve as input parameters for the generative adversarial network (GAN), facilitate the filtering process. The system also calculates both the structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR), performing model updates via backward propagation. This technique utilizes deep learning technologies to achieve rapid, real-time filtering of underwater cable images. The experimental results reveal that the loss function of the CNN reaches 0.16 with an accuracy of 97.5%, while the loss function of the adversarial GAN network approaches 0.05. Compared with traditional methods such as DDN, JORDOR, RESCAN, and PRENet, the proposed CNN-GAN algorithm exhibits superior performance, as evidenced by the higher PSNR and SSIM values. Specifically, for clear water images, the PSNR reaches 29.86 dB and the SSIM is 0.9045. For severely polluted images, the PSNR is 28.67 dB and the SSIM is 0.8965, while for unevenly illuminated images, the PSNR and SSIM values are 24.37 dB and 0.88, respectively. These enhancements significantly benefit the monitoring and maintenance of power systems.