Maintenance Practices Research Articles

A Review on Traditional and Artificial Intelligence-Based Preservation Techniques for Oil Painting Artworks.

Oil paintings represent significant cultural heritage, as they embody human creativity and historical narratives. The preservation of these invaluable artifacts requires effective maintenance practices to ensure their longevity and integrity. Despite their inherent durability, oil paintings are susceptible to mechanical damage and chemical deterioration, necessitating rigorous conservation efforts. Traditional preservation techniques that have been developed over centuries involve surface treatment, structural stabilization, and gel-based cleaning to maintain both the integrity and aesthetic appeal of these artworks. Recent advances in artificial intelligence (AI)-powered predictive maintenance techniques offer innovative solutions to predict and prevent deterioration. By integrating image analysis and environmental monitoring, AI-based models provide valuable insights into painting preservation. This review comprehensively analyzes traditional and AI-based techniques for oil painting maintenance, highlighting the importance of adopting innovative approaches. By integrating traditional expertise with AI technology, conservators can enhance their capacity to maintain and preserve these cultural treasures for future generations.

The Application of Infrared Thermography for Enhancing Maintenance in Public University

A university’s main function is to produce quality graduates, and in doing so, university buildings do play significant roles and are vital assets and resources. Hence, to prolong a university building lifecycle to ensure all university activities to continue performing at an optimum level, building maintenance management is central. Eventhough previous studies have been conducted in this area, university buildings maintenance is still viewed as insignificant. The research about utilization of infrared thermography in term of maintenance in public universities is still lacking. The objective of this research was to provide recommendations for areas within the public universities that can benefit from the application of infrared thermography to enhance maintenance. The research method used in this research was a qualitative method. Three public universities that were established for more than 50 years were chosen as case studies. The data was collected through narrative review and then analyzed. The findings highlighted the benefits of implementing infrared thermography in older building maintenance within public universities, particularly in improving energy efficiency and identifying hidden issues such as moisture intrusion and structural defects. The study emphasized the importance of timely maintenance actions based on infrared thermography assessments to reduce energy consumption and apply predictive maintenance strategies. By conducting in-depth analyses at three established public universities in Malaysia, the researchers demonstrate the effectiveness of infrared thermography in enhancing maintenance practices and prolonging the lifecycle of university buildings.

The impact of sustainable design and construction upon FM: four case studies

Purpose This paper aims to analyze the impact of sustainability measures taken during the design and construction phases, by examining two categories of sustainability: energy efficiency and reducing carbon emissions and material selection and waste management. These aspects are examined from the perspectives of long-term building performance and maintenance practices, as well as user/tenant satisfaction. Design/methodology/approach This study includes a literature review related to the two topics under consideration, followed by a comparative case study analysis of four projects to determine practical validity. All case studies in this paper used a semi-structured survey with various project stakeholders, which helped the authors identify measures taken as well as obstacles and challenges during the process. Findings According to the four case studies, adequate attention should be paid to the two areas of interest during a project’s design and construction phases. Including case studies from around the world (four case studies from three different countries) offers insights into effective sustainability practices in building design and construction, providing instances of successful implementation and emphasizing the obstacles and potential when incorporating sustainability into the design and construction phases. Research limitations/implications The findings also show that design and construction participants and companies should reduce waste generation and carbon emissions. In addition, they should make decisions on material selection to enhance projects’ sustainability and to contribute to creating a habitable planet for the future. Originality/value The influence of the design and construction phases on long-term project sustainability is of major importance and concern to users, owners, designers, contractors and facility managers. This study illustrates the necessity of including sustainability measures in the design and construction phases, highlighting the importance of sustainability in building design and construction through effective implementation techniques and interdisciplinary teamwork to realize sustainable goals.

Malaysian homestay experience programme: A qualitative investigation on operator's crisis management practices and resiliency strategies

Understanding the specific crisis management practices and resilience strategies homestay operators adopt during a crisis is critical. Currently, it is an area that remains under-researched despite the growing importance of resilience in community-based tourism operations. As such, this study investigates the crisis management practices and strategies for resiliency that homestay operators adopt during a health crisis. This qualitative study has been conducted via semi-structured interviews among ten prominent homestay operators in Malaysia. The thematic data analysis using an inductive approach was employed to ensure the transferability of the findings. The study found that marketing initiatives, pandemic prevention measures, maintenance practices, and government assistance were the best crisis management practices homestay operators could implement during the pandemic phase. Homestay operators have adopted four resilience strategies to survive after the pandemic: for other creative business opportunities, government assistance, use of technology innovation, togetherness in mitigating the pandemic, and industry engagement and collaboration. The results provide comprehensive strategies to the policymakers and industry stakeholders to strengthen homestay businesses in the future. Such understanding enables government efforts to maintain CBT sustainability, which is in line with the National Tourism Policy (2020–2030) and Sustainable Development Goals (SDG) 1 (No Poverty), 8 (Decent Work and Economic Growth) and 11 (Sustainable Cities and Communities). The results are also expected to assist homestay operators in re-strategizing and striving toward recovery and resiliency after the crisis.

PRESCRIPTIVE MAINTENANCE SYNCHRONIZED TO THE CHARACTERISTICS OF INDUSTRY 5.0: A THEORETICAL DISCUSSION

This article aims to examine and understand the current scope of Prescriptive Maintenance integrated with the human-centric characteristics of Industry 5.0. Therefore, the main research question is the following: “In the view of the managers of the companies visited, located in the Southeast Region of Brazil, what are the main technical and administrative difficulties that companies encounter in successfully implementing proactive combinations on the factory floor, such as Prescriptive Maintenance integrated with the characteristics of Industry 5.0”?. The evolution of industrial processes in recent decades and the advent of Industry 4.0 integrated with human-centered characteristics have placed maintenance as one of the protagonists of industrial processes. In a scenario where automation and humans are gaining more and more space, the future of industries lies in Prescriptive Maintenance. To achieve the objective of the article, professional experiences from participation in congresses, seminars and technical visits to several factories located in the Southeast Region of Brazil are used. In data collection, we explored documentary observation and bibliographic research in the Science Direct and Web of Science databases. The study focuses on research areas related to maintenance integration, Industry 4.0 (Techno centric) and human-centric characteristics (Industry 5.0). The article brought important conclusions, as it addresses the impact and trends on the factory floor so that managers can develop competitiveness and productivity. The article highlights the role of maintenance methods in improving factory floor performance, emphasizing human knowledge and Prescriptive Maintenance practices to ensure operations within World Class standards.

Machine Learning Models for Predictive Maintenance in Industrial Engineering

Purpose: The general objective of this study was to investigate machine learning models for predictive maintenance in industrial engineering. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library.Findings: The findings reveal that there exists a contextual and methodological gap relating to machine learning models for predictive maintenance in industrial engineering. The research highlighted the transformative potential of machine learning models in optimizing predictive maintenance for industrial engineering, demonstrating significant reductions in unplanned downtime and maintenance costs. It identified the strengths of various machine learning approaches, such as supervised, unsupervised, and reinforcement learning, in predicting equipment failures and optimizing maintenance schedules. Despite the benefits, challenges such as data quality, integration complexity, and the need for specialized skills were noted. Future advancements in machine learning, IoT data, and computational power were expected to further enhance predictive maintenance systems, making them more accurate, efficient, and widely adopted across industries.Unique Contribution to Theory, Practice and Policy: The Systems Theory, Diffusion of Innovations Theory and Resource-Based View (RBV) Theory may be used to anchor future studies on machine learning models for predictive maintenance in industrial engineering. This study provided several recommendations that contributed to theory, practice, and policy. It emphasized the development of hybrid machine learning models, integration of domain-specific knowledge, and real-time data collection using IoT technologies. It suggested standardized data protocols and personnel training for better implementation and efficiency. Policy recommendations included regulatory frameworks, incentives for technology adoption, data sharing, and robust data privacy guidelines. These contributions aimed to enhance the accuracy and applicability of predictive maintenance models, improve industrial maintenance practices, and support technological innovation through supportive policies.

Hybrid fault diagnostics and prognostics system development for motor bearings health monitoring

The article focuses on developing a specialized prognostics system for motor bearings to ensure the safety and reliability of motors and generators. Timely maintenance improves both equipment health and economic viability. The primary objective is to predict early-stage faults, anticipate degradation, and analyze the Remaining Useful Life (RUL) without interrupting the system. The versatile system is developed to continuously monitors deviation profiles, calculates RUL, and optimizes the industrial performance. To address the persistent challenge of imprecise model, integrating sensor data and understanding the physical context, the proposed framework combines data-driven and model-driven learning approaches. The hybrid diagnostics and prognostics system employ Convolutional-LSTM (CNN-LSTM) for feature extraction and fault diagnostics. Particle Filtering (PF) is integrated for stochastic degradation state prediction and RUL estimation. The framework prioritizes learning degradation trends from sensor data without direct physical integration. To validate the effectiveness of the proposed scheme, experiments were performed using the popular PROGNOSTIA bearings dataset, confirming its superior performance in RUL prediction. The developed prognostics system will find broad applicability in process monitoring across various industries, advancing the field and optimizing maintenance practices.

Application of Automated Pavement Inspection Technology in Provincial Highway Pavement Maintenance Decision-Making

Taiwan’s provincial highways span approximately 5000 km and are crucial for connecting cities and towns. As pavement deteriorates over time and maintenance funds are limited, efficient pavement inspection and maintenance decision-making are challenging. Traditional inspections rely on manual visual assessments, consuming significant human resources and time without providing quantitative results. This study addresses current maintenance practices by introducing automated pavement damage detection technology to replace manual surveys. This technology significantly improves inspection efficiency and reduces costs. For example, traditional methods inspect 1 km per day, while automated survey vehicles cover 4 km per day, increasing efficiency fourfold. Additionally, automated surveys reduce inspection costs per kilometer by about 1.7 times, lowering long-term operational costs. Inspection results include the crack rate, rut depth, and roughness (IRI). Using K-means clustering analysis, maintenance thresholds for these indicators are established for decision-making. This method is applied to real cases and validated against actual maintenance decisions, showing that the introduced detection technology efficiently and objectively guides maintenance decisions and meets the needs of maintenance units. Finally, the inspection results are integrated into a pavement management platform, allowing direct maintenance decision-making and significantly enhancing management efficiency.

Towards rational decision-making on repointing to mitigate moisture damage in building envelopes: A probabilistic study

Repointing is a prevalent maintenance practice in Northern Europe aimed at mitigating moisture-related damage in brick masonry buildings. Although commonly used, evidence of its effectiveness is limited. This study assesses repointing's role in reducing damage risks by conducting a probabilistic hygrothermal analysis of two wall types: timber frame walls and masonry cavity walls. Results indicate that repointing could reduce the mold index in timber frame walls and moisture content in the autoclaved aerated concrete (AAC) layer of masonry cavity walls, particularly in walls with visible defects. However, its impact is minimal on walls without significant deficiencies. Moreover, the study suggests that repointing, given its labor-intensive and costly nature, may not always be the most judicious maintenance strategy. It recommends a selective repointing approach, suiting the specific conditions and needs of the wall based on its location, orientation, and existing state, rather than a blanket application across all façade sections.

A Comprehensive Study on Building Maintenance Practices for Heritage Shop Houses in Penang, Malaysia

A Comprehensive Study on Building Maintenance Practices for Heritage Shop Houses in Penang, Malaysia

Ship Maintenance Management Practices: Insights from Internships in Shipping Engineering

This research investigates ship maintenance management practices among senior students in shipping engineering during their internships at a vocational school in Jakarta. The study explores the challenges faced, strategies employed, and lessons learned in managing maintenance tasks onboard ships. Seven participants were interviewed, highlighting technical malfunctions, resource limitations, time constraints, environmental factors, and communication barriers as primary challenges. Participants utilized preventive maintenance, cross-training, task prioritization, collaborative problem-solving, and digital tools to enhance operational efficiency. Lessons underscored the importance of preparation, teamwork, adaptability, continuous learning, and communication skills in navigating maritime maintenance complexities. The findings emphasize the role of proactive maintenance strategies and technological integration in optimizing sustainability and operational reliability within the maritime industry. Integrating practical training into educational curricula and embracing digital innovations are recommended for preparing future professionals. Future research could explore larger sample sizes and quantitative methodologies to validate findings and quantify the impact of maintenance practices. This study contributes to enhancing vocational education in maritime sciences and informs industry practices for advancing ship maintenance management globally.

Research Paper on Artificial Intelligence

Artificial Intelligence (AI) has emerged as a transformative force across various sectors, revolutionizing processes, enhancing efficiency, and redefining innovation. This research paper delves into the multifaceted landscape of AI, focusing on its applications, knowledge representation, and implications for innovation. The paper begins by exploring the diverse applications of AI across healthcare, gaming, finance, data security, social media, robotics, and e-commerce. In healthcare, AI aids in diagnosis and patient care, while in gaming, it enables strategic game play and enhances user experience. The finance sector leverages AI for automation, analytics, and algorithmic trading, improving decision-making and customer service. AI also plays a vital role in ensuring data security through advanced detection systems, manages vast social media data for enhanced user engagement, and drives innovation in robotics and e-commerce. Moving forward, the paper delves into the realm of expert systems and knowledge representation, elucidating the role of AI in simulating human expertise and modeling complex information structures. It discusses various aspects of knowledge representation, such as propositional knowledge representation, image retrieval, functional relationships between objects, and class representation formalism, highlighting their significance in developing intelligent systems. Furthermore, the paper examines the integration of AI in maintenance practices, both for tangible systems like engineering workshops and intangible products like data extraction wrappers. It underscores the importance of AI in optimizing operational efficiency, reducing downtime, and ensuring continuous data extraction. Lastly, the paper explores the concept of deep learning as a general- purpose invention, discussing its potential implications for innovation, management, institutions, and policy. It addresses key issues such as the management and organization of innovation, intellectual property rights, competition policy, and the cumulative knowledge production facilitated by deep learning. In conclusion, this research paper provides a comprehensive overview of AI's transformative potential, emphasizing the need for further research and analysis to fully comprehend its impact on society, economy, and innovation.

Evaluation of ride quality in a passenger car with non-uniform tyre radial stiffness

This study is dedicated to examining the influence of varying radial stiffness along the circumference of tyres on a road vehicle ride quality and with a specific focus on aspects relevant to maintenance. Computer simulations employing a three-dimensional road-vehicle model, developed within commercial multi-body software, were conducted to explore the multifaceted factors influencing vehicle behaviour. These factors encompass road unevenness, driving speed, and the range of radial force variation resulting from harmonic changes in tire radial stiffness. Furthermore, simulation tests were carried out using a model of a diagnostic wheel balancing machine, which holds significance in vehicle maintenance practices. Measurements obtained from a passenger car equipped with standard tires with minimal non-uniformity, served as reference values for assessing the car's vibration levels. Additionally, selected scenarios were examined, involving vehicle with multiple wheels exhibiting non-uniform radial stiffness. The findings underscore that non-uniform radial stiffness can magnify the detrimental effects of road irregularities, potentially leading to amplified vibration levels and a heightened need for maintenance considerations.

Transforming experimental radiology: Design and implementation of an innovative ePACS image storage system for AI imaging research environments

Introduction and purpose: We present the needs, design, development, implementation, and accessibility of a crafted experimental PACS (ePACS) system to securely store images, ensuring efficiency and ease of use for AI processing, specifically tailored for research scenarios, including phantoms, animal and human studies and quality assurance (QA) exams. The ePACS system plays a crucial role in any medical imaging departments that handle non-care profile studies, such as protocol adjustments and dummy runs. By effectively segregating non-care profile studies from the healthcare assistance, the ePACS usefully prevents errors both in clinical practice and storage security. Methods and results: The developed ePACS system considers the best practices for management, maintenance, access, long-term storage and backups, regulatory audits, and economic aspects. Moreover, key aspects of the ePACS system include the design of data flows with a focus on incorporating data security and privacy, access control and levels based on user profiles, internal data management policies, standardized architecture, infrastructure and application monitorization and traceability, and periodic backup policies. A new tool called DicomStudiesQA has been developed to standardize the analysis of DICOM studies. The tool automatically identifies, extracts, and renames series using a consistent nomenclature. It also detects corrupted images and merges separated dynamic series that were initially split, allowing for streamlined post-processing. Discussion and conclusions: The developed ePACS system encompasses a successful implementation, both in hospital and research environments, showcasing its transformative nature and the challenging yet crucial transfer of knowledge to industry. This underscores the practicality and real-world applicability of our innovative approach, highlighting the significant impact it has on the field of experimental radiology.

EVALUATION OF MAINTENANCE EFFECTIVENESS AND HUMAN FACTORS: CASE STUDY OF A RESEARCH INSTITUTE

Maintenance management is an integral part of any organization’s overall commercial process that develops administrative operational value. Thus, the maintenance function is of immense importance to ensure the safety and effective operation of a facility and equipment in compliance with a statutory obligation, to maximize equipment life, and to reduce the risk of failure. However, for maintenance success work planning, the perception of human factors, and the interrelation of workers and groups in any organisation must be understood. This in turn would determine the allocation of maintenance tasks to team members. This research focused on the evaluation of key performance indicators in relation to the maintenance effectiveness of selected equipment, in a research institution. The impact of human factors on the maintenance was also investigated. Key performance indicators such as Mean Time to Repair (MTTR), Mean Time Between Failure (MTBF), downtime, repair time, and availability were used to quantitatively evaluate the maintenance effectiveness of the equipment, while the systematic approach to empirical research was used to analyse the effect of human factors on maintenance practices. This study was carried out over a period of 6 months on a 4-tonne hydraulic lift, hydraulic hoist jack lift, tire changing machine, fault diagnostic machine, and computerized alignment gauge. Data collected from the maintenance unit was analysed. A total of 11.92 hours was recorded as the MTTR of the five machines. The machines had a high level of availability and a total downtime of 12.01 hours out of operational time of 2369.8 hours over a period of 6 months. The level of motivation and competence among maintenance personnel was assessed to be very good. This is expected to have contributed largely to the effective maintenance practices of workers in the unit. From this study, it can be deduced that the level of motivation and competence is high among maintenance personnel. In terms of productivity, effectiveness, teamwork, decision-making, execution, safety, and responsiveness, the human factor has been shown to have thrived well. This in turn, stimulated the corresponding increase in the availability of machines over the 6 months- of study.

How to commit aging workers: the role of HR bundles

Since workforces are aging worldwide, scientists and practitioners aim at understanding the potential contribution of (bundles of) HR practices in increasing commitment among aging workers. In this time-lagged survey study, we distinguished four HR bundles for aging workers (Kooij et al., 2014a; Van Dalen et al., 2015): development, maintenance, utilization, and accommodative HR practices. We investigated the influence of the availability and use of these HR bundles on commitment over time and the role of age in this relationship. Based on two-wave complete panel data (N = 489) collected among employees in a Dutch university, we found that perceived availability of maintenance HR practices was positively associated with commitment over time. Further, based on the Selection, Optimization, and Compensation model (Baltes & Baltes, 1990), we expected and found that the relationship of the perceived availability and the perceived usage of utilization HR practices with commitment strengthens with age. These results highlight the importance of different bundles of HR practices for increasing commitment over time among aging workers.

Harvesting condensate wastewater from commercial buildings air handling unit (AHU) drainage: An opportunity for water conservation

Water wastage is a matter of great concern, especially in this era of rapid development. Water consumption in high-rise and large-sized buildings is very high. Furthermore, high-rise buildings use a lot of water to generate the use of HVAC (Heating, Ventilation, and Air Conditioning) machines. However, the waste of water from this machine is also very disturbing. Therefore, it is important for us to realize that various ways and technologies can be used and utilized to prevent waste from happening. This research focuses on the wastage of water and waste water from air handling unit machines in high-rise commercial buildings around Penang, Malaysia. To make this study a success, several methods have been used, including interviews, observation at the study site, and conducting research through newspapers, journals, reports, and so on. Through this method, data to be analyzed can be collected. In this study, several factors lead to the waste of water from the Air Handling Unit machine, which will have adverse effects on the machine, the building, and also the environment such as condensate and drainage. To overcome the problem, it is important to implement proactive routine maintenance practices for the HVAC system to reduce the AHU wastewater. The finding incorporation of water-saving methods, such as using AHU wastewater for other non-potable purposes, is a practical and ecologically appropriate approach. This research emphasizes the potential for large water savings and environmental advantages in commercial buildings, calling for greater adoption of such methods in facility management and building operations.

Software maintenance practices using agile methods towards cloud environment: A systematic mapping

AbstractAgile methods have emerged to overcome the obstacles of structured methodologies, such as the waterfall, prototype, spiral, and so on. There are studies showing the usefulness of agile approaches in software development. However, studies on Agile maintenance are very limited in number. Regardless of the chosen methodology, software maintenance can be carried out in either a local (on‐the‐premise) or global (distributed) environment. In a local environment, the software maintenance team is co‐located on the same premises, while in a global environment, the team is geographically dispersed from the customer. The main objective of this Systematic Mapping (SM) study is to identify the practices useful for software maintenance using the Agile approaches in the Cloud environment. We have conducted a comprehensive search in well‐known digital databases and examined the articles that map to the pre‐defined inclusion criteria. The study selected and analyzed 48 articles out of 320 published between 2000 and 2022. The findings of the mapping study reveal that Agile can resolve the major issues faced in traditional software maintenance, making the role of this approach significant in global/distributed software maintenance. Cloud computing plays a vital role in software maintenance. Most of the studies highlight the application of XP‐ and Scrum‐based Agile maintenance models. The study found a need for more Agile maintenance solutions in the cloud, highlighting the importance of agile in software maintenance, both locally and globally. Irrespective of the environment, Cloud computing provides a centralized platform for collaboration and communication, while also offering scalability and flexibility to adapt to diverse infrastructure needs. This allows agile maintenance practices to be implemented across both local and global environments, leveraging the cloud's capabilities to overcome geographical and infrastructural challenges.

Defect monitoring system of the internal structures of a sodium fast reactor using an artificial intelligence model

This study developed a defect-monitoring system with an artificial intelligence model, YOLOv7, which is tailored for processing image data from an ultrasonic visualization system within sodium fast reactor (SFR) internal structures. For the safety of SFR internal structures, although it is a crucial inspection for defect monitoring, it is difficult to identify structural defects because of the invisible environment. Therefore, we applied the YOLOv7 model in this study; however, we encountered challenges including decreased accuracy with complex defect shapes and complications from data augmentation during pre-training. To solve these problems, we additionally applied the enhanced super-resolution generative adversarial network for higher resolution and a Sobel noise-filtering algorithm to enhance the defect detection accuracy. And we evaluated our system by comparing it with a confidence score. This underscores the effectiveness of the approach in enhancing the defect detection capabilities. Therefore, this defect-monitoring system should be designed to preemptively identify internal structure deformations and enhance SFR safety and maintenance practices.

Development of operational standards procedures for maintenance and maintenance of public railway infrastructure infrastructure and electrical mechanical and architectural components in Wbs-based station buildings to improve maintenance and maintenance performance

A railway station is a place of departure and stoppage for trains. Poor, inappropriate maintenance and upkeep of infrastructure without strict supervision can result in physical damage to the infrastructure itself, thus losing its initial value and reducing the service life of the infrastructure. If the maintenance and upkeep of infrastructure in the transportation sector is not done properly, it can cause delays or, in the worst case, accidents. To ensure a high level of reliability and availability, the implementation of an effective maintenance program is very important. This is then clarified if poor maintenance and upkeep practices, such as in the transportation sector, can also affect the reliability of the transportation system and even cause life-threatening accidents. Damage or accidents that occur can also lower the company's image and customer or consumer trust. This research aims to develop Standard Operating Procedures (SOP) for maintenance and upkeep work on station buildings, architectural components, and mechanical electrical. The method used is based on a Literature Study, survey, and case study. The result of this research is the development of SOP maintenance and upkeep work on architectural components and mechanical electrical to improve maintenance performance.