Maintenance Management Practices Research Articles

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.

Automated analysis and assignment of maintenance work orders using natural language processing

Maintenance management forms a core component of building facility management, which is particularly important for specialized and complex healthcare facilities. However, existing maintenance management tends to rely on manual processing, which is time-consuming and human error-prone. Recorded maintenance work orders (MWOs) are recognized as good potential data source to support maintenance activities. In this paper, the authors propose a comprehensive framework utilizing natural language processing (NLP) techniques to automate the interrogation of textual data of MWOs that assist in developing maintenance material management and preventive maintenance strategies and transforming maintenance worker assignment from a labor-intensive process to a more automated one. A set of historical MWOs from a hospital was used for model development, and our model achieved an accuracy of 0.83 on worker assignment. This study addresses the difficulty of utilizing work order information due to the unstructured nature of textual data and contributes to better maintenance management practices in buildings.

Soil carbon sequestration in building life cycle assessment: Offsetting measure or site impact

Abstract The environmental impacts of the built environment typically focus on the materials and operations within the building envelope. Little, if any, consideration is given to impacts that occur outside the building. This can be an appropriate simplification for urban settings where the building dominants the site. However, it would ignore impacts and benefits associated with site activities in more rural settings. The present study investigates soil carbon sequestration (SCS) principles to determine whether SCS can be considered an offsetting measure for buildings or should be considered within the site impacts for a project. Soil organic carbon (SOC) levels change overtime in response to external factors and interventions, however this response time is within the reference study period commonly used for building-scale life cycle assessments (LCAs). Therefore, it would be plausible to monitor changes in SOC throughout the lifespan for a project. Currently, there are some emerging methods but no consensus standards on accounting for SCS in building LCAs and current methodologies for quantifying SCS need further development to align with carbon offset principles. However, since soil is an intrinsic part of the landscape, it would be appropriate to incorporate SCS within the use phase impacts for a site. Expanding the system boundary to account for SCS should include accounting for the environmental impacts associated with landscaping, maintenance, and land management practices. Guidelines for calculating SCS and landscaping environmental impacts need to be developed to better reflect the complete environmental impact of the built environment.

The Influence of Business Analytics and Big Data on Predictive Maintenance and Asset Management

This study investigates the impact of business analytics and big data on predictive maintenance and asset management practices within the energy industry in Indonesia. A quantitative research approach, utilizing a survey methodology, was employed to gather data from stakeholders representing various sectors of the energy industry. The study analyzed the relationships between business analytics, big data, predictive maintenance, and asset management using structural equation modeling (SEM) with Partial Least Squares (PLS) regression. The results indicate significant positive relationships between the utilization of business analytics and big data and various performance metrics, including asset reliability, operational efficiency, and cost savings. Furthermore, organizational factors such as leadership support and data quality were found to play a crucial role in facilitating the adoption and implementation of predictive maintenance strategies. The findings underscore the transformative potential of data-driven maintenance strategies in enhancing operational efficiency, reducing downtime, and improving asset reliability within the Indonesian energy industry.

Characterizing Deep Learning Package Supply Chains in PyPI: Domains, Clusters, and Disengagement

Deep learning (DL) frameworks have become the cornerstone of the rapidly developing DL field. Through installation dependencies specified in the distribution metadata, numerous packages directly or transitively depend on DL frameworks, layer after layer, forming DL package supply chains (SCs), which are critical for DL frameworks to remain competitive. However, vital knowledge on how to nurture and sustain DL package SCs is still lacking. Achieving this knowledge may help DL frameworks formulate effective measures to strengthen their SCs to remain competitive and shed light on dependency issues and practices in the DL SC for researchers and practitioners. In this paper, we explore the domains, clusters, and disengagement of packages in two representative PyPI DL package SCs to bridge this knowledge gap. We analyze the metadata of nearly six million PyPI package distributions and construct version-sensitive SCs for two popular DL frameworks: TensorFlow and PyTorch. We find that popular packages (measured by the number of monthly downloads) in the two SCs cover 34 domains belonging to eight categories. Applications , Infrastructure , and Sciences categories account for over 85% of popular packages in either SC and TensorFlow and PyTorch SC have developed specializations on Infrastructure and Applications packages, respectively. We employ the Leiden community detection algorithm and detect 131 and 100 clusters in the two SCs. The clusters mainly exhibit four shapes: Arrow, Star, Tree, and Forest with increasing dependency complexity. Most clusters are Arrow or Star, while Tree and Forest clusters account for most packages (Tensorflow SC: 70.7%, PyTorch SC: 92.9%). We identify three groups of reasons why packages disengage from the SC (i.e., remove the DL framework and its dependents from their installation dependencies): dependency issues, functional improvements, and ease of installation. The most common reason in TensorFlow SC is dependency incompatibility and in PyTorch SC is to simplify functionalities and reduce installation size. Our study provides rich implications for DL framework vendors, researchers, and practitioners on the maintenance and dependency management practices of PyPI DL SCs.

A STUDY ON COLD CHAIN MAINTENANCE AND VACCINE MANAGEMENT PRACTICES OF URBAN GOVERNMENT HEALTH FACILITIES IN JABALPUR DISTRICT, INDIA

Introduction: Efficient cold chain and vaccine logistics management are made possible by the health system's processes, practices, and policies, which are evaluated by Effective Vaccine Management, a diagnostic tool. A significant number of urban Jabalpur children receive vaccinations from a government health facility.The aim of the study wasto Assessthe cold chain maintenance and vaccine management practices at urban government Cold Chain Points of Jabalpur district.Methods:A cross-sectional study was carried out at all of 13 urban government ColdChain Points across Jabalpur district. Globally validated World Health Organization -United Nations International Children's Emergency Fund Effective Vaccine Management tool was used for this study. Criteria wise and Consolidated Effective Vaccine Management scores were calculated as per World Health Organization guidelines. Results: All the urban service point stores or cold chain points scored more than the target 80% in two criteria: storage capacity and building. The consolidated criteria score of all the cold chain points in urban Jabalpur, criteria like maintenance, stock management, management information system, and supportive functions, were lagging behind the 80% mark. Low scores in maintenance, stock management, and management information system and supportive functions criteria are somehow a setback for all the preparationand hard work done towards optimal vaccine management and supply chain in the area. Conclusion: The overall performance of the urban cold chain points in Jabalpur district was far better as compared to the state-level effective vaccine management assessment done in Madhya Pradesh in 2011 and 2016. Planned preventive maintenance for buildings and equipment needs to be strengthened. Issues like Lack of information for stock management, need for periodic refresher trainings are to be emphasized.

Critical factors that influence the effectiveness of facility maintenance management practice in public university buildings in Ethiopia: an exploratory factor analysis

Critical factors that influence the effectiveness of facility maintenance management practice in public university buildings in Ethiopia: an exploratory factor analysis

Investigation of work order processing in different facilities: a questionnaire-based survey

PurposeThe aim of this paper was to study current practices in FM work order processing to support and improve decision-making. Processing and prioritizing work orders constitute a critical part of facilities and maintenance management practices given the large amount of work orders submitted daily. User-driven approaches (UDAs) are currently more prevalent for processing and prioritizing work orders but have challenges including inconsistency and subjectivity. Data-driven approaches can provide an advantage over user-driven ones in work-order processing; however, specific data requirements need to be identified to collect and process the functional data needed while achieving more consistent and accurate results.Design/methodology/approachThis paper presents the findings of an online survey conducted with facility management (FM) experts who are directly or indirectly involved in processing work orders in building maintenance.FindingsThe findings reflect the current practices of 71 survey participants on data requirements, criteria selection, rankings, with current shortcomings and challenges in prioritizing work orders. In addition, differences between criteria and their ranking within participants’ experience, facility types and facility sizes are investigated. The findings of the study provide a snapshot of the current practices in FM work order processing, which aids in developing a comprehensive framework to support data-driven decision-making and address the challenges with UDAs.Originality/valueAlthough previous studies have explored the use of selected criteria for processing and prioritizing work orders, this paper investigated a comprehensive list of criteria used by various facilities for processing work orders. Furthermore, previous studies are focused on the processing and prioritization stage, whereas this paper explored the data collected following the completion of the maintenance tasks and the benefits it can provide for processing future work orders. In addition, previous studies have focused on one specific stage of work order processing, whereas this paper investigated the common data between different stages of work order processing for enhanced FM.

天然气长输管道维护管理在企业降本增效中的实践与探索

天然气长输管道维护管理在企业降本增效中的实践与探索

Strategic Reliability Centered Maintenance Decision Analysis Model Using Fuzzy and Analytic Hierarchy Process for Refinery Valves

Modern industries, particularly oil and gas, necessitate robust maintenance management and optimization for increased profits, plant availability, and maintenance cost reduction. The maintenance strategy is therefore crucial, especially under global economic pressures on equipment reliability. This study examines the present maintenance management practices for refinery valves, critical equipment whose failure can have severe consequences. Many existing models for selecting critical equipment in maintenance management face limitations due to inadequate decision analysis and exclusion of key criteria. This research proposes a strategy model, combining artificial intelligence and multi-criteria decision-making techniques like Fuzzy Logic and Analytical Hierarchy Process, to fill this gap and manage refinery valves' maintenance. Proven by case studies and interviews, the model improves safety, reliability, operational availability and enables operators to monitor their decision consequences. The model can convince asset managers to invest in maintenance initiatives.

Smart Heating, Ventilating, Air-conditioning and Refrigeration by Web-based Geographic Information System

Objective: Heating, ventilating, air-conditioning and refrigeration (HVAC&R) systems currently account for a significant portion of energy consumption. The HVAC&R system contributes the largest energy consumption in a building, so it is essential to optimize energy consumption to improve energy saving worldwide. Methods: The web-based geographic information system (GIS) enables the seamless sharing of spatial data across the globe, accessible anytime and anywhere via the World Wide Web. The set of remote reading networked sensors, advanced modems, and data loggers facilitate the intercommunication for the geodatabase of HVAC&R’s facilities. The integration of remote sensing technology and the Internet of Things, grounded in GIS establishes a control loop dedicated to energy conservation. This method is a pioneering concept in control science, offering significant potential for enhancing design, maintenance, and energy management practices. It empowers energy users with real-time control over their energy consumption, making a substantial advancement in this field. Results: In this work, the model of HVAC&R control in context with web-based GIS showed that the regression mathematical analysis in compliance with the computational method holds the capacity to predict energy consumption and evaluate energy loss. Conclusion: In regression analysis, the P was found to be 0.991 for the percentage of dissatisfaction, 0.977 for energy use intensity, and 0.962 for data envelopment analysis efficiency. Additionally, the curve estimation showed that the power function was utilized in regression analysis processes.

Synergies between Lean and Industry 4.0 for Enhanced Maintenance Management in Sustainable Operations: A Model Proposal

Companies actively seek innovative tools and methodologies to enhance operations and meet customer demands. Maintenance plays a crucial role in achieving such objectives. This study identifies existing models that combine Lean Philosophy and Industry 4.0 principles to enhance decision-making and activities related to maintenance management. A comprehensive literature review on key concepts of Lean Philosophy and Industry 4.0, as well as an in-depth analysis of existing models that integrate these principles, is performed. An innovative model based on the synergies between Lean Philosophy and Industry 4.0, named the Maintenance Management in Sustainable Operations (MMSO) model, is proposed. A pilot test of the application of the MMSO model on a conveyor belt led to an operational time increase from 82.3% to 87.7%, indicating a notable 6.6% improvement. The MMSO model significantly enhanced maintenance management, facilitating the collection, processing, and visualization of data via internet-connected devices. Through this integration, various benefits are achieved, including improved flexibility, efficiency, and effectiveness in addressing market needs. This study highlights the value of integrating Lean Philosophy and Industry 4.0 principles to improve maintenance management practices. The proposed MMSO model effectively leverages these principles, fostering agility, optimized resource utilization, heightened productivity and quality, and reduced energy consumption. The model not only serves as a tool for operational optimization and customer demand enhancement but also aligns with sustainability principles within the energy transition. Its successful application in the pilot test phase further reinforces its potential as a reliable approach for maintenance management and sustainable operations in both production and decision-making processes.

Maintenance Management Practices and Factors Affecting Maintenance of Private University Buildings in Ogun State Nigeria

Building maintenance management is an efficient and viable technique used for addressing issues with construction's upkeep. Nigerian tertiary institutions have been accused of lacking maintenance culture. Meanwhile, quality and sustainable education is delivered when educational buildings are adequately maintained. The present study seeks to investigate the factors affecting maintenance management of private university buildings in Ogun state Nigeria. Questionnaire survey was utilized as a research instrument to gather information from the maintenance personnel within private universities in Ogun state, Nigeria. The data analysis techniques used were descriptive and inferential statistics. The results revealed that use of poor quality components and materials,no adoption of appropriate maintenance cycle for building maintenance, lack of discernable maintenance culture in the country, and inadequate training and development of maintenance personnel are the top most four factors affecting maintenance management of private university buildings in Ogun state, Nigeria. The study provides insights into the factors affecting maintenance management of university buildings. An understanding of these factors would help tertiary institutions and maintenance personnel facilitate the development of strategies required in minimizing the factors affecting maintenance management practices in tertiary institutions.

Reconciling Pavement Condition Data from Connected Vehicles with the International Roughness Index from Standard Monitoring Equipment Using Physics-Integrated Machine Learning

This study investigates the possibility for augmenting infrequent standard pavement survey data with spatiotemporally continuous sensor data crowdsourced from connected vehicles. A framework is proposed to leverage physics-integrated machine learning (ML) models to reconcile the two disparate data sources. The proposed approach is demonstrated for the International Roughness Index (IRI). Using the quarter-car simulation, vertical acceleration data records were generated based on random stratified sampling from probability distributions of vehicle suspension properties at different speed levels on elevation profiles collected from various pavement sections. An ML model was pretrained to approximate the quarter-car simulation. Using transfer learning, the parameters of the outer layers of the model were finetuned to adapt model output to the measured values, while the parameters of the inner layers were fixed to preserve the embedded knowledge of the physical behavior of the suspension. The resulting physics-integrated ML model can predict the standard IRI value of a pavement section using speed, suspension properties, and features of the acceleration response from vehicles traversing that road section. The evaluation results on independent training and test datasets showed high accuracy, precision, and generalization capability, especially on smoother road profiles. This is attributed to the limited training data for rougher roads. After averaging the predicted IRI from all vehicles passing a certain road segment, a correction function is used to rectify the model’s under-predicted output and estimate the actual IRI value for that segment. High-frequency data resulting from the proposed approach can enhance pavement design, maintenance management, and asset management practices.

Determining the Essential Criteria for Choosing Appropriate Methods for Maintenance and Repair of Iraqi Healthcare Building Facilities

Today, building maintenance and repair (M&R) is a neglected aspect of the construction business throughout a building’s entire life cycle. Selecting appropriate M&R strategies is crucial, particularly for emerging economies like Iraq with severely constrained resources. This study seeks to identify the primary selection criteria for M&R methods of healthcare building facilities (HBFs) in Iraq. A comprehensive desktop literature analysis was undertaken to extract and determine the essential selection criteria for the most suited M&R approaches to buildings in general. Then, two rounds of the Delphi survey were conducted to consolidate the specific selection criteria to suit the circumstances of Iraq and HBFs. A total of 21 sub-criteria were identified and divided into six main groups. The main criteria and the associated sub-criteria were then analyzed and ranked using the fuzzy analytic hierarchy process (FAHP) technique. The ranking of the various main criteria revealed that the “cost” criterion was ranked first in terms of importance, followed by the “human resources” and “quality” criteria. The fourth, fifth, and sixth main criteria are “reliability/flexibility”, “safety/risk/environment”, and “facilities/technology”, respectively. The overall ranking of the sub-criteria placed “optimization and cost reduction” in the first position and “extending the life of the equipment and preserving their initial quality” in the bottom place. It is anticipated that the key findings and effective recommendations of this study will considerably contribute to the improvement of building maintenance and repair management practices in developing nations while enhancing different stakeholders’ understanding of the most important selection criteria for M&R methods, particularly with regard to healthcare building facilities in Iraq.

Municipal street maintenance challenges and management practices in Sweden

The municipal street network acts as a multifunctional asset by providing people, vehicles and public services with a well-functioning infrastructure. To keep it in good condition, optimal maintenance measures are required which would result in an efficient use of taxpayers’ money. This paper investigates the street network deterioration processes and the management practices that the municipal administrations have applied in Sweden. The study is based on a survey with Swedish municipalities using questionnaires and complementary interviews. The answers provide insight into a wide range of common pavement distresses and deterioration factors, along with pavement management practices. The study identifies that potholes, surface unevenness and alligator cracking are the most cited challenges, while pavement ageing, heavy traffic and patches are the most noted causes. Similarly, the cold climate and population density are influential factors in pavement deterioration. Allocation of the maintenance and rehabilitation and reconstruction budget is higher in the northern part of the country as well as in densely populated municipalities. Condition data collection and use of commercial Pavement Management Systems (PMS) are limited. Addressing the challenges effectively may be possible through the enhancement of the budget, feasible/clear guidelines from municipal councils/politicians, and reducing the gap between street network administrations and utility service providers.

Assessment of Maintenance Management Practice in Government-owned Tertiary Institution

The state of utilities and infrastructure within an academic institution plays a vital role in learning and the development of human capital. Hence, this study seeks to examine the management practices used in maintaining facilities in a government-owned tertiary institution and then examine the conditions of critical facilities and their related services in the institution. To achieve the study's goal, a questionnaire survey approach was used to collect information about users' and property managers' perceptions of maintenance management practices. The results revealed the existence of a maintenance policy covering critical systems and services. The discovery also implies that maintenance tasks are generated and planned in response to inspections and user requests. Respondents' perceptions of the state of facilities and utilities indicate that they are in good condition. While literature shows school infrastructure issues remain an ongoing concern, the majority of respondents affirm security and electricity as the most important critical infrastructure and services in the institution, amongst others. The most important limitation of the current study lies in the fact that the findings may not be generalizable to other tertiary institutions in Nigeria. Hence, more research needs to be done in order to gather more information about maintenance management practices in public universities and other educational institutions to improve critical facilities and services to enhance learning outcomes.

Maintenance and asset management practices of industrial assets: importance of tribological practices and digital tools

There are a large number of rotating and sliding parts in industrial assets. Tribological behaviour plays a significant role in influencing friction and wear, and in turn, the life of these parts. There are issues and challenges in understanding the tribological aspects and behaviour of machine components by maintenance professionals so that informed decisions can taken to improve performance and productivity. An understanding of tribology helps in developing and applying the tools and techniques necessary for better maintenance. In recent years, remote performance monitoring (RPM), internet of things (IoT), machine learning, artificial intelligence and data analytics have made a significant contribution to maintenance and asset management. This paper reviews the tribological aspects related to maintenance, reliability and asset management. The findings of this study will be useful to engineers and managers to understand and appreciate the relationship between tribology, maintenance, reliability and availability for better asset management.

Literature Review on Gravel Road Maintenance: Current State and Directions for Future Research

Gravel roads form a significant share of the global road network, usually in sparsely populated rural areas. They are important, especially in agriculture, tourism, and forestry, connecting rural to urban areas. This systematic literature study comprises 105 reviewed publications on gravel road maintenance. Review articles on maintenance management practices, especially concerning objective condition assessment and data-driven methods (DDMs), are lacking. Therefore, this review provides a concise overview of current gravel road maintenance practices and ongoing research on objective condition assessment and DDMs for gravel road maintenance. It offers researchers in gravel road maintenance and other related fields a clear indication of where to focus their research efforts, as it suggests the direction for future research. Visual assessment methods are predominant for monitoring the condition of gravel roads, while objective methods and DDMs are not common. Research on gravel roads and their maintenance has increased in the last two decades, especially in North America and Northern Europe. Condition assessment is shifting from subjective to objective methods, utilizing knowledge from technological advancements in image processing, vibration and acoustics analysis, and so forth. There are some excellent research initiatives for objectively assessing the condition of gravel roads and DDMs, but the practical implementation is limited. Implementing objective assessment methods and DDMs generally improves the management of gravel roads with regard to decision-making, maintenance costs, safety, and the stability and comfort of the ride. Objective condition assessment and DDMs have the potential to enhance maintenance practices in the maintenance of gravel roads.

Defects in Malaysian hospital buildings

PurposeHospital building maintenance management constitutes a pertinent issue of global concern for all healthcare stakeholders. In Malaysia, the maintenance management of hospital buildings is instrumental to the Government’s goal of providing efficient healthcare services to the Government's citizenry. However, there is a paucity of studies that have comprehensively explored all dimensions of hospital building defects in relation to maintenance management. Consequently, this study seeks to evaluate the defects of hospital buildings in Malaysia with the aim of proffering viable solutions for the rectification and prevention of the issue.Design/methodology/approachThe study utilised a quantitative approach for data collection.FindingsThe findings indicated that cracked floors, floor tile failures, wall tiles failure, blocked water closets, and damaged windows were some of the flaws that degrade hospital buildings. The study’s outcomes reveal that defects not only deface the aesthetic appearance of hospital buildings but also inhibit the functionality of the buildings and depreciate the overall satisfaction.Research limitations/implicationsConsidering the indispensable role of hospital buildings in the grand scheme of healthcare service provision and ensuring the well-being of people, the issue of defects necessitates an urgent re-evaluation of the maintenance management practices of hospital buildings in Malaysia. Previous studies on the maintenance management of hospital buildings in Malaysia have focused primarily on design, safety, and construction.Practical implicationsThis is particularly important because defects in hospital buildings across the country have recently led to incessant ceiling collapses, fire outbreaks, ceiling, roof collapses, and other structural failures. These problems are typically the result of poor maintenance management, exacerbated by poor design and construction. These disasters pose significant risks to the lives of hospital building users.Originality/valueThis study offers invaluable insights for maintenance organisations and maintenance department staff who are genuinely interested in improving hospital buildings’ maintenance management to optimise staff's performance and enhance the user satisfaction of hospital buildings in Malaysia and globally.