Maintenance Techniques Research Articles

Machine Learning in Industrial Applications: An In-Depth Review and Future Directions

As industries undergo a paradigm shift towards automation and data-driven decision-making, the integration of machine learning (ML) technologies has emerged as a pivotal force in optimizing industrial processes. This paper presents a comprehensive review of the current state of ML applications in various industrial domains, encompassing manufacturing, logistics, energy, and more. The analysis delves into the diverse methodologies and algorithms employed, highlighting their strengths and limitations. The review explores the impact of ML on enhancing efficiency, reducing costs, and improving overall performance in industrial settings. Real-world case studies demonstrate successful implementations of ML techniques for predictive maintenance, quality control, supply chain optimization, and process optimization. Additionally, the paper investigates the challenges and bottlenecks encountered during the deployment of ML in industrial contexts, such as data quality issues, interpretability, and scalability. Furthermore, this review identifies key trends and emerging technologies shaping the future of ML in industrial applications. Areas such as edge computing, federated learning, and explainable AI are discussed as potential solutions to current challenges. The paper also explores the integration of ML with other emerging technologies like the Internet of Things (IoT) and blockchain to create holistic and robust industrial ecosystems. In conclusion, this in-depth review provides a comprehensive understanding of the current landscape of ML in industrial applications, offering valuable insights for researchers, practitioners, and decision-makers. The outlined future directions aim to guide the development and implementation of ML solutions in the industrial sector, fostering innovation and efficiency in the evolving landscape of smart and connected industries.

PENATAAN JENIS TANAMAN BERBASIS HOME GARDEN UNTUK MENDUKUNG KELESTARIAN SUMBER PAKAN TRIGONA DI DESA BUKIT JANGKRIK

The Trigona honey bee farming business has rapidly developed in Bukit Jangkrik Village due to its simple cultivation and maintenance techniques. The success of this cultivation heavily depends on the availability of feed sources throughout the year, which are generally obtained from home garden-based yards. Unfortunately, the community's knowledge about the types of nectar, pollen, and resin-producing plants as food sources is still lacking, so their feeding needs have not yet been met. In order to support the sustainability of Trigona's food sources, PGRI Mahadewa University Indonesia is conducting community service activities by creating a demonstration plot of an agroforestry-based home garden model. This model combines annual fruit-bearing plants such as Durian, Matoa, Kelengkeng, Mangga, Murbei, Jeruk, Anggur, and Jambu biji, with flowering plants that produce nectar, pollen, and resin throughout the season, such as Matahari, Air Mata Pengantin, Asoka, and Kastuba. The results of the activities show the success of this demonstration plot in providing a sustainable food source, thanks to the strategic and potential arrangement of plant types. In conclusion, this agroforestry-based home garden model serves as a practical solution that not only supports the sustainability of Trigona bee cultivation but also involves the community in maintaining the local ecosystem through the planting of multifunctional plants.

UML DIAGRAMS OF THE MANAGEMENT SYSTEM OF MAINTENANCE STATIONS

Vehicle uptime is becoming increasingly important as transportation solutions become more complex and the transportation industry looks for new ways to be competitive. Traditional fleet management systems are gradually being extended with new features to improve reliability, such as better maintenance planning. Typical diagnostic and predictive maintenance techniques require extensive experimentation and simulation during development. This is not possible for the entire vehicle as it would require too many engineering resources. The developed diagrams presented in the article reflect the processes that take place at service stations. Various factors affecting quality and speed, such as the availability or ordering of spare parts, were taken into account when developing the service charts. With the help of the proposed diagrams, the client can independently decide whether to use the services of an authorized car service. Before using the services of a car service, it is advisable to familiarize yourself with the information presented on the website. This will help you choose a car service that meets all your expectations in terms of professional assistance. It is worth looking for a service station combined with an inspection station. Then you can fully check the performance of the car.

Requirements are All You Need: The Final Frontier for End-User Software Engineering

What if end users could own the software development life cycle from conception to deployment using only requirements expressed in language, images, video or audio? We explore this idea, building on the capabilities that Generative Artificial Intelligence brings to software generation and maintenance techniques. How could designing software in this way better serve end users? What are the implications of this process for the future of end-user software engineering and the software development life cycle? We discuss the research needed to bridge the gap between where we are today and these imagined systems of the future.

Advancements in Predictive Maintenance: A Bibliometric Review of Diagnostic Models Using Machine Learning Techniques

This bibliometric review investigates the advancements in machine learning techniques for predictive maintenance, focusing on the use of Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) for fault detection in wheelset axle bearings. Using data from Scopus and Web of Science, the review analyses key trends, influential publications, and significant contributions to the field from 2000 to 2024. The findings highlight the performance of ANNs in handling large datasets and modelling complex, non-linear relationships, as well as the high accuracy of SVMs in fault classification tasks, particularly with small-to-medium-sized datasets. However, the study also identifies several limitations, including the dependency on high-quality data, significant computational resource requirements, limited model adaptability, interpretability challenges, and practical implementation complexities. This review provides valuable insights for researchers and engineers, guiding the selection of appropriate diagnostic models and highlighting opportunities for future research. Addressing the identified limitations is crucial for the broader adoption and effectiveness of machine learning-based predictive maintenance strategies across various industrial contexts.

Digital Twins for Improving Proactive Maintenance Management

Proactive maintenance is a policy aimed at identifying the root cause of failure and correcting it before it causes other problems and leads to machinery failure and breakdown. Implementing this policy can enhance reliability, availability, maintainability, and safety (RAMS) at low cost. A digital twin (DT) is a digital copy of a physical object and its applications will play a leading role in the future of smart manufacturing. DT concept is increasingly appearing in industrial applications including proactive maintenance, enabling accurate identification of equipment condition, proactive prediction of faults, and enhanced reliability. This review paper focuses on the performance and applications of different aspects of DTs in proactive maintenance polices. The review of literature focused on the applications of DT in maintenance management for improving equipment RAMS. The literature review shows that the application of DT techniques in proactive maintenance remains very important for managing the maintenance of critical equipment and production systems. Several DT frameworks for proactive maintenance have been discussed. Furthermore, this study provides a comprehensive roadmap for future research initiatives aiming to fully utilize the capabilities of technology design teams. Finally, the results of this study will be of value to professionals who want and aspire to implement technological design to achieve maintenance excellence.

EFFECTS OF TRAIN SERVICE OPERATION QUALITY ON CUSTOMER SATISFACTION IN CASE OF DIRE DAWA STATION

This study evaluates the operational performance and customer satisfaction of the Ethio-Djibouti Standard Gauge Railway (EDR) through insights from 160 employee surveys, revealing strengths in technical quality and adherence to health, safety, and environmental standards, yet highlighting critical challenges such as suboptimal equipment utilization and customer service responsiveness that affect user satisfaction. Key performance indicators from regression analysis show that technical quality (Beta = 0.331), health and safety standards (Beta = 0.344), and asset management practices (Beta = 0.336) are strong predictors of customer satisfaction, with information technology (Beta = 0.241) also playing a crucial role in enhancing operational efficiency. To address these issues, the study recommends implementing advanced maintenance techniques, improving staff training for customer interaction, and upgrading technological infrastructure, while optimizing asset management practices to improve resource allocation. Overall, focusing on these areas is vital for EDR to enhance operational efficiency and customer satisfaction, thereby securing its competitive position in the transportation sector.

User Perceptions and Conservation Practices: A Case Study of Maintenance Strategies at S. Bento Railway Station

Located in the heart of Porto, Portugal, the S. Bento train station is renowned worldwide for its architectural splendour and historical significance. Inaugurated in 1916, this UNESCO World Heritage Site presents stunning ceramic tile panels and architecture influenced by contemporary French design. This study presents a comprehensive historical analysis of the conservation state of S. Bento station, detailing observed anomalies, their origins, probable causes, and the maintenance and rehabilitation techniques employed over the years. Moreover, it explores the relationship between conservation practices and tourist perceptions of the station, focusing on how rehabilitation efforts influence user satisfaction. This analysis was carried out through a comprehensive sentiment analysis of over 4000 tourist reviews between 2011 and 2023, and data from the station management entity, providing insights into the effectiveness of these interventions. The research contributes to the broader discussion on heritage conservation, offering recommendations for future maintenance strategies that integrate user expectations and sentiment.

Advanced Artificial Intelligence Drivers and IoT Network Systems for Data Collection and Network Transmission in the Industry 5.0 Era

The primary bases of Industry 5.0, the Internet of Things (IoT), artificial intelligence (AI), and big data (BD) may serve as a basis for applying the concepts of smart manufacturing, innovative goods and services, and predictive maintenance (PDM) into practice. Assessing the remaining useful life of components using failure data and state monitoring information constitutes the AI techniques for predictive maintenance (PDM), which are critical in Industry 5.0. However, it is sometimes challenging to identify the annotation of component usage, creating an open problem for obtaining accurately labeled information and understanding it. This problem needs to be rectified if Industry 5.0 progress is proceeded with. In order to solve the PDM job, this study presents a new data-driven decision system (DDS) based on AI that overcomes the earlier issue. It does by concentrating on an actual industrial use scenario that involves modern processing and monitoring machines. Specifically, the most fundamental components of the recommended DDS include cloud-based storage, data interpretation, predictive modeling, recognition of features using Principal Component Analysis (PCA), and data collecting. An efficient prediction model called the unique moth flame search-tuned light gradient boosting machine (MFS-LGBM) technique is presented in this work. By enabling the ongoing gathering of identified samples and offering a data analysis environment to assist the maintainer/operator, the suggested approach resolves the PDM task. The research findings showed that when compared to the previous approaches, the suggested approach accomplishes the greatest conceivable balance between comprehension, computational effort, and forecasting accuracy in terms of quality estimation. Based on the suggested methodology, the performance is examined in terms of RMSE (600.10), MAE (390.60), [Formula: see text] (0.790) and MAPE (25.70) measures. By incorporating it into the cloud structure, the suggested technique minimizes maintenance expenses and enhances production efficiency by improving machining processes, assisting maintainers, and delivering current operating risk alerts.

Factors influencing smallholder participation in commercial tree growing outgrower schemes: The case of Namwasa outgrower scheme in Uganda

Over the past 30 years, outgrower/contract farming has been promoted as an institutional innovation to boost agricultural and farm forestry in less developed countries. Understanding factors that make outgrower arrangements beneficial is crucial to realizing their full potential. This study examines a commercial tree-growing outgrower scheme in Uganda, assessing socioeconomic factors influencing farmers' participation and their motivations. Data collection utilized a mixed-method approach, including semi-structured interviews (n = 80), focus group discussions, and key informant interviews. Findings revealed that participation was significantly associated with the purpose for the trees (χ2 = 19.7875, p= 0.00005), membership in farmer-based organizations (χ2 = 76.05, p= 2.77E-18), and household size (χ2 = 14.12667, p= 0.000856). Key motivations were access to quality seedlings, rated very important by 95 % of respondents, and farm credits, rated very important and important by 43.8 % and 36.3 % of farmers, respectively. Additionally, 96.3 % and 95 % considered training in agrochemical application and in planting, maintenance, and harvesting techniques, respectively, as very important. Most farmers (93 %) preferred on-spot cash payment over payment in kind. 55 % of farmers preferred donation with charge while 45 % preferred purchasing inputs at a subsidized price. Most farmers (65 %) favored a cost-sharing arrangement for harvesting costs, and 98 % wanted the company to cover post-harvest expenses. This study highlights the need of considering farmers' socioeconomic characteristics and preferences for contractual arrangements when designing outgrower schemes, as well providing seedlings, financial support, and training to enhance participation.

The Suez Canal Incident: Learning from Failure by Using Reliability and Maintenance Techniques

The purpose of this paper is to investigate the Suez Canal incident by formulating a hybrid model, inspired by maintenance and reliability techniques, that can enable the process of learning from failures. The hybrid model uses a fault tree analysis (FTA), reliability block diagram (RBD), cut set analysis (CSA), and the bowtie method (BTM). The formulated techniques show the synergy between the techniques and how they can be incorporated in order to provide a better understanding of the incident and extract the lessons learned that can prevent a reoccurrence of such an incident. The need for a such hybrid model is essential nowadays since incidents and disasters are becoming more complex, hence a deep analytic technique is required to cover all reliability factors. It can be argued that there is no perfection in using only one technique to address all the reliability aspects, and it is vital to integrate more than one technique, with its particular advantage, in order to have a clear, focused and comprehensive understanding of the area of concern. In addition, it is essential to derive from that level of analysis lessons that can help to improve the overall system reliability and prevent future undesired events.

Optimizing Sustainable Landscape Maintenance Techniques with Digital Tools and Data Analytics

As urban environments continue to grow and face the challenges of resource constraints, the need for efficient and sustainable landscape maintenance practices becomes increasingly evident. This research paper explores integrating digital tools and data analytics to optimize sustainable landscape maintenance techniques, foster resource-efficient practices, and promote ecological preservation. The study focuses on implementing cutting-edge technologies, such as Internet of Things (IoT) sensors, remote monitoring systems, and data-driven decision-making, to enhance the management and maintenance of urban landscapes. By leveraging real-time data and advanced analytics, landscape architects can make informed decisions about water usage, fertilization, irrigation schedules, and overall landscape health. The paper investigates various case studies and pilot projects from the Arab region, showcasing successful implementations of digital tools in landscape maintenance. It analyzes the impact of these technologies on water conservation, energy efficiency, and the reduction of carbon footprints associated with landscape maintenance operations. Moreover, the research highlights landscape architects’ role in seamlessly integrating digital tools into the design and maintenance process. By adopting data-driven strategies, landscape professionals can optimize resource allocation, minimize waste, and enhance the overall resilience of urban landscapes. Finally, this research paper demonstrates that the integration of digital tools and data analytics presents a promising opportunity for enhancing the sustainability and effectiveness of landscape maintenance practices in the Arab region. By embracing these innovations, stakeholders in landscape architecture can contribute to the creation of greener, more resilient, and environmentally conscious urban environments.

Maintenance of dental bleaching: A systematic review

After bleaching procedure, color pigments can adhere to the rough dental surface and can trigger more discoloration. In this sense, no standard post-bleaching maintenance technique exists. The purpose of this study was to evaluate the maintenance of enamel color after bleaching procedure through a systematic review. The research question was defined as follows: Is the tooth whitening maintenance necessary after a bleaching treatment? Two reviewers conducted the literature search in PubMed (MEDLINE), Web of Science, Scopus, EMBASE, and SciELO. Both in vitro and clinical trials reporting the color change of bleached enamel after the use of a tooth whitening agent were included in the review. The risk of bias in the included manuscripts was evaluated using the RoBDEMAT tool. A total of 11 articles were included in the qualitative analysis. Due to the lack of groups for comparison, a meta-analysis could not be conducted. The whitening maintenance procedures identified were remineralizing agents, antioxidants, over-the-counter agents, toothpaste containing Blue Covarine, casein phosphopeptide-amorphous calcium phosphate fluoride paste, 0.05% sodium fluoride mouthwash, arginine, nano-hydroxyapatite, nano-carbonate apatite, and CO2 laser. In conclusion, tooth whitening maintenance could be achieved by using some agents. The association of these agents might be beneficial for maintaining the color of the enamel throughout 12 months following bleaching procedure.

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.

Guía de procedimiento asistencial de enfermería para la inserción, mantenimiento y retiro de la sonda nasogástrica u orogástrica

The lack of unified criteria in the practice of nursing care can lead to errors during procedures. For this reason, the present nursing care procedure guide aims to standardize the correct form of the technique of placement, maintenance and removal of the nasogastric tube or orogastric tube, maintaining quality standards. This will allow to favor its permanence, reduce complications related to displacement and skin lesions. Descriptive study and literature review. The search for information was carried out in high-impact journals and other reliable media such as Scopus, ProQuest, EBSCO, Scielo, repositories and Google Scholar. This review addresses the indications, contraindications, necessary materials and equipment, stages of the procedure, complications and recommendations for the correct insertion, maintenance and removal of nasogastric and orogastric tubes.

Predictive Maintenance of Induction Motor in the Cutting Section of Paper Industry

Induction Motors are vital to the paper industry because they power a variety of equipment needed for pulp processing, drying and cutting among other tasks. Ensuring the consistent manufacture of these motors and satisfying market demands depends heavily on their dependability. However, the extreme humidity, dust and fluctuating loads that occur in the paper sector present serious obstacles to the efficiency and durability of induction motors. To maintain continuous production and satisfy consumer demand, the paper industry significantly depends on the effective operation of a variety of machinery including induction motors in the cutting area. Unexpected malfunctions in these vital parts however might result in expensive downtime and lost output. By using data-driven strategies that anticipate and avoid breakdowns before they happen, predictive maintenance techniques provide a proactive way to reduce such risks. This report provides an extensive analysis of the application of predictive maintenance techniques designed especially for induction motors in the paper industry’s cutting division. The predictive maintenance techniques includes Random Forest Tree, Linear Regression and Support Vector Machines algorithm with these algorithm the Induction Motor’s variations in temperature, vibration, sound, and speed parameters were collected and trained for predicting the failure. Among these algorithms Support Vector Machines shows greater advantage in predicting the failure with the accuracy of 84% whereas Random Forest Tree with 75% and Linear regression with 72%. As well as the real time data’s were collected and stored in the database. The performance of the Induction Motor were efficiently improved and monitored under normal and fault condition by Machine Learning techniques.

Modelling indirect tensile strength of warm mix asphalt with variable reclaimed asphalt pavement (RAP) content

Introduction. There is a world-wide trend to also increase the sustainability of the road sector. The growing use of various industrial by-products, together with economical and eco-friendly construction and maintenance techniques can be observed in many countries. Problem Statement. The utilization of warm mix asphalt and the use of relatively high share of reclaimed asphalt materials in new asphalt mixtures can have negative features, as well. Purpose. Modelling indirect tensile strength of warm mix asphalt with variable reclaimed asphalt pavement (RAP) content was aimed at based on Hungarian laboratory test series.

Web-Based Maintenance Prediction of Machine Conditions and Failure Modes Using Machine Learning

Machine failure may result in unplanned downtime, production losses, safety risks, and increased costs. Hence, predicting machine failures is a significant challenge faced by many industries. Predictive Maintenance (PdM) techniques can help mitigate these risks by predicting machine failures before they occur. This research, therefore, develops two supervised Machine Learning (ML) algorithms to predict failure conditions and the associated failure modes for a milling machine. Six machine features were investigated. The ML models were then developed using a four-stage process including data pre-processing, training, evaluation, and deployment. Several ML algorithms were applied then the results were evaluated using accuracy, recall, precision, and [Formula: see text]1-score. The results showed that the Random Forest algorithm was the most effective in predicting machine conditions and failure modes of the milling machine. A web application for PdM was finally developed and tested for the PdM of the milling machine. In conclusion, the developed web application including ML algorithms can support the effective PdM for the machine, which leads to enhancing its availability and improving productivity. Future research considers adopting ML algorithms for predicting machine conditions and failure types of other machines.

Exploration of oral hygiene practices, oral health status, and related quality of life of individuals residing in the Rorya district of Tanzania, East Africa.

Oral health substantially impacts individuals' quality of life, making it an important target for global health interventions. This research describes oral health status, practices, and beliefs within the Rorya district of Tanzania to understand barriers to care. To quantify physical oral health status, intraoral examinations were conducted on adults, noting the Decayed Missing and Filled Teeth (DMFT) and Comprehensive Periodontal Inflammatory Burden Index (CPBI). Oral Health-Related Quality of Life (OHRQoL) and semi-structured interviews were conducted to understand oral hygiene behaviors and beliefs. Data was analyzed via two-sample t-tests, Pearson's statistics, and NVIVO. A purposive sample (n = 139) of participants self-reported to reside in either Burere (n = 32), Nyambogo (n = 52), or Roche (n = 55) were assessed. A two-sample t-test revealed females (n = 67; x¯ = 7.1; SD = 5.4; p < 0.05) have a significantly higher DMFT score than males (n = 72; x¯ = 3.7; SD = 3.9). Moreover, the OHRQoL score of females (n = 67; x¯ = 12.10; SD = 14; p < 0.05) were significantly higher than males (n = 72; x¯ = 10.16; SD = 3). In contrast, males have significantly higher CPBI scores (x¯ = 3.8; SD = 1.5; p=<0.05) than females (x¯ = 3.0; SD = 1.3). Additionally, older age groups presented higher GI and PISA scores, while the younger group (20-30 years) displayed the highest mean DMFT score. The themes that emerged from semi-structured interviews were "pearls of laughter guarded by wisdom teeth," "whispered tales of oral tides and communal echoes," and "tales of the tooth fairy." In this community, proper oral health maintenance techniques are vital yet frequently disregarded, mainly due to disparities in access to resources, reflected in oral health scores. Addressing this is a crucial intervention, presenting an opportunity to uplift overall well-being. Moreover, gender and age disparities in oral health highlight the urgent need for tailored interventions.

Performance Assessment of Basic Oxygen Furnace Slag (BOFS) as an Ice-Melting Abrasive Material

Abrasives play a crucial role in surface blasting, especially in cold climates, where snow and ice significantly challenge transportation infrastructure and road safety. The main purpose of this research is to address the critical need for effective and sustainable winter maintenance techniques. This study examined the possibilities of Basic Oxygen Furnace Slag (BOFS) as a substitute (an abrasive substance) for conventional aggregates in ice-melting applications. Thus, this research assessed the physical properties of BOFS, such as absorption capacity, tested at -5°C, and aggregate angularity test, and designed for evaluation of the surface texture, friction, and percentage of fractured faces in uncompacted voids (SSD%) of the aggregates. Moreover, the potential use of a blend of BOFS with de-icing salts, specifically sodium chloride (NaCl) and calcium chloride (CaCl2), was investigated as an effective ice-melting agent. For this purpose, three tests were carried out: the petri dish test according to SHRP H-205.1, the polishing ice melting test using a modified ASTM C 944 (rotational cutter), and the surface temperature measurement test. By assessing the performance of BOFS, our goal was to justify its efficacy, offering a practical alternative for regions experiencing severe winter conditions. Eventually, the findings from this study assert that BOFS can be used for surface blasting, indicating its potential as a substitute for traditional abrasives.