Maintenance Prioritization Research Articles

Risk-based maintenance strategy for oil transfer pipelines using an intuitionistic fuzzy computational approach

The safety and reliability of oil transfer pipelines are crucial, as pipeline failures can severely affect the environment. As pipeline risk management is dynamic and uncertain, traditional maintenance approaches often fall short. This research paper proposes an Intuitive Fuzzy Computational Approach (IFCA) for oil transfer pipeline maintenance (RBM). To develop a targeted and nuanced maintenance plan, Failure Modes and Effects Analysis (FMEA) is employed. Pipeline equipment is hierarchically organised according to their failure mode criticality, which guides maintenance design. This paper leverages the context of an Interval Valued Intuitionistic Fuzzy (IVIF) environment to streamline the hierarchical classification of oil transmission pipeline instruments. The objectives are threefold: (1) to develop an intuitionistic fuzzy risk assessment model, (2) to design a modified algorithm for maintenance prioritisation, and (3) to develop a decision support system. Additionally, the research incorporates the IVIF-SWARA method to enable flexible adjustments in the alignment of the pipeline instruments. By addressing the specific challenges of oil transfer pipelines, this research aims to contribute to advancing the field of pipeline maintenance and risk management. The proposed RBM strategy, underpinned by the IFCA framework, is expected to enhance pipeline reliability and mitigate the risks associated with pipeline failures. Abbreviations: RBM: Risk-Based Maintenance; IFCA: Intuitionistic Fuzzy Computational Approach; FMEA: Failure Modes and Effects Analysis; IVIF: Interval Valued Intuitionistic Fuzzy; SWARA: Stepwise Weight Assessment Ratio Analysis; RCM: Reliability-Centered Maintenance; RBI: Risk-Based Inspection; CBM: Condition-Based Maintenance; IFS: Intuitionistic Fuzzy Set; HFPR: Hesitant Fuzzy Preference Relation; IVIFPHA: Interval-Valued Intuitionistic Fuzzy Power Heronian Aggregation; MCDM: Multi-Criteria Decision-Making; S C O i : Accuracy Function of Occurrence; S C S i : Accuracy Function of Severity; K ( a ~ ) : Failure Probability Index; L W D : Minimum Allowable Weight of The Failure Detection Index; WS, WO, WD: Weight Assigned to The Failure Outcome; RP V j : Relative Priority Value; OREDA: The Offshore Reliable Data Handbook; IVIFPWHA: Interval-Valued Intuitionistic Fuzzy Power Weight Heronian Aggregation

Strategies of Public University Building Maintenance—A Literature Survey

In this article, comprehensive insights into the field of building maintenance, emphasizing the importance of keywords, collaborative efforts among authors, and the evolving research landscape, are provided. The use stage, as the longest phase in a building’s life cycle, involves economic, technical, and social activities. Numerous authors have contributed to the broader topic of building maintenance, covering aspects such as maintenance prioritization, improvement actions, decision-making guidelines, factors affecting success, cost estimation models, and BIM usage in maintenance. The study conducts a thorough literature review using Scopus as a search engine, employing the full-counting method for authorship, and uses VOSviewer 1.6.20 software for bibliometric analysis to identify gaps and outline future research directions. The results reveal distinct data sets focusing on building maintenance from general to specific, with each set reflecting unique aspects and considerations. Bibliometric maps illustrate author collaboration and keyword connections within each set, highlighting evolving research trends and areas of focus. Overall, the analysis highlights the importance of prioritizing maintenance, integrating technology, and fostering collaboration to enhance building maintenance in educational institutions. The study concludes with recommendations for future research, emphasizing the ongoing need for exploration and development in building maintenance, especially on maintenance strategies and Life Cycle Costs of university buildings.

Classifying flexible pavement defects using hybrid machine learning approach

The transportation infrastructure sector significantly impacts a country’s gross domestic product (GDP), particularly in developing nations striving to manage and maintain road networks as valuable assets. While asset generation is integral, the more intricate challenge lies in effective maintenance. Pavement monitoring, a crucial component of pavement maintenance and management systems (PMMS), evaluates defect severity, road maintenance prioritization, and maintenance types. To enhance road health monitoring, the present study introduces a hybrid machine learning (ML) method, integrating support vector machine (SVM) and convolutional neural network (CNN). The proposed semi-automated detection system aims to reduce human supervision in traditional surveys, thereby cutting down the cost of pavement distress maintenance The research utilizes data collected by the authors from Ahmedabad city, Gujarat, following Indian road congress (IRC) guidelines for defect selection. Training involves 1,000 images for each crack type, with testing on 100 images. Results indicate that the SVM-CNN model achieves 87% accuracy in training and 91% accuracy in testing for road defect classification, showcasing its efficiency in pavement maintenance and management. The system presents the potential to significantly enhance the efficiency of road maintenance processes, making it a valuable asset for developing nations striving for a more streamlined approach to road network preservation.

The Use of Multi-Criteria Parameters for Bridge Maintenance Prioritization and Maintenance Categorization Options in Tanzania

A bridge is a structure across physical obstacles like valleys, rivers, seas, oceans, lakes, roads, and railways. Its components get damaged when exposed to harsh conditions resulting from various causes, which reduces its lifetime. Maintenance is necessary to prolong its remaining service life once it gets damaged. Successful maintenance depends on various factors, including the availability of funds, the effectiveness of condition assessment, and other damaging factors. Many developing countries face a scarcity of financial resources to restore the functions of bridges at network levels. In order to have a better plan for financial resources, it is important to devise a methodology for prioritising bridge maintenance at the network level. This research used parameter ratings to determine the Bridge Prioritisation Index (BPI) derived from selected parameters affecting bridge performance. The parameters considered for this study are bridge defects, earthquakes, traffic actions, and scouring potential. The prioritisation indices were determined from four bridges, which are Kikwete, Mvomero, Unkuku, and Nyahua. The bridge ratings from bridge condition indices were determined to be 2 for Unkuku and Mvomero bridges and 1 for Kikwete and Nyahua bridges. Scour ratings from scour indices were determined to be 4 for the Unkuku, Nyahua, and Mvomero bridges and 3 for the Kikwete bridge. Seismic ratings from seismic indices were determined to be 1 for all four bridges, and traffic ratings from traffic indices were determined to be 3 for Unkuku and Nyahua bridges and 2 for Mvomero and Kikwete bridges. Bridge Prioritisation Indices (BPI) determined indicate that Unkuku Bridge has higher priorities in maintenance than other bridges, with a BPI of 2.19, followed by Mvomero Bridge with a BPI of 2.08, Nyahua Bridge with a BPI of 1.67, and lastly, Kikwete Bridge with a BPI of 1.41. Based on the obtained BPI, the maintenance action required was proposed to be preventive maintenance for the Unkuku, Nyahua, and Mvomero bridges and routine maintenance for the Kikwete bridge. Further studies are proposed to include the earthquake amplitude in bridge maintenance prioritisation.

The application of OREDA (offshore reliability data) in developing valve criticality analysis criteria

In oil and gas processing plants, extensive networks of hundreds to thousands of valves are vital in controlling the entire process. However, not all valves have the same importance in process control. Therefore, identifying and prioritizing critical valves is essential for efficient and safe operation. Assessing the importance of valves requires a risk-based approach. However, quantifying the associated risks becomes challenging, especially when limited field data makes it difficult to model the probability of valve failure. One possible solution is to use Offshore Reliability Data (OREDA) as a valuable failure probability data source. OREDA provides valuable insight into historical failure and performance data related to oil and gas equipment such as valves. To go one step further, the new proposal presented in this paper includes implementing a modified OREDA approach that considers the cumulative operating time of the valve. By considering cumulative operating hours, a more comprehensive and accurate assessment of failure probability can be achieved, enabling more informed decision-making regarding valve criticality and prioritization of maintenance.

A Clustering-based Approach for Maintenance Prioritization of Medical Devices in a New Hospital

Medical devices are fundamental to preventing, diagnosing and treating disease and high availability of them is vital for the uninterrupted operation of a hospital. That is why hospitals should plan and carry out maintenance activities to keep their medical devices in a healthy operating condition. The effectiveness of these activities can be increased by determining the maintenance priorities of devices. On the other hand, setting individual priorities for each device becomes complicated when a hospital has hundreds of medical devices. In this concern, grouping medical devices and determining group-based maintenance priorities will be more advantageous for maintenance planning. In this study, a novel approach is proposed for the maintenance prioritization of medical devices in a new hospital. In the proposed approach, first prioritization attributes are defined and weighted using the analytical hierarchy process (AHP). Then, medical devices are grouped based on the predetermined attributes by using data clustering. Finally, maintenance priorities of medical device clusters are determined based on the weighted sum of cluster centers.

Sustainable Prioritization of Public Asphalt Paved Road Maintenance

The development of transportation infrastructure is crucial for a country's progress as it enables access to social and economic amenities and acts as a vital link between production and consumption. This is particularly significant for landlocked nations like Zambia, which heavily rely on a wide network of public highways, including both paved and unsurfaced roads. Regardless of their quality and construction, all road surfaces degrade over time due to the combined effects of traffic load and environmental factors. If not properly managed, this can lead to costly repairs and rehabilitation efforts. Government organizations face a dilemma when deciding which public roads to prioritize for maintenance. This follows that the inherent hurdle is competing needs within limited finances for infrastructure development. However, the challenge of road maintenance prioritization can be addressed by adopting a multi-criteria approach. This study aims to address the issue of sustainability and provides insights into which public roads should be given priority for maintenance. The research proposes a comprehensive set of criteria focusing on sustainability and assigns relative importance to them, with emphasis on state of deterioration, emergency function, climate resilience, environment, strategic importance, social considerations, economic factors, and political importance. It suggests that road maintenance decision-making should prioritize sustainability, resilience, and societal well-being, challenging traditional assumptions.

Bridge Structural Reliability for Maintenance Prioritization

A significant percentage of assets of the Italian national bridge stock have outlived their nominal design service lives, a situation that has led to an increasing number of structural failures over the years, e.g. the deadly 2018 collapse of Morandi Bridge in Genoa. It would have been impossible to replace all existing infrastructural assets, so the focus was shifted to their upkeep. This is commonly achieved through structural health assessments of bridges and through properly planned and prioritized maintenance interventions. For a proper management of their bridge stock, infrastructure managers need a prioritization tool capable of quickly assessing whether a particular unit is in immediate need of maintenance. Currently, the prioritization of each bridge inside a stock is often based on its structural reliability. Yet, whilst several complex and time-consuming structural reliability assessment methodologies exist, these often fail to be quick in application. The present article provides for this shortcoming by introducing a novel simplified structural reliability model. In particular, it provides a framework for the assessment of overload, degradation, seismic, hydraulic and landslide risk factors and a detailed formalization of the most widespread, i.e. overload and degradation. On the basis of such a swift model, infrastructure managers can quickly detect rapidly deteriorating bridges, guarantee well-timed interventions and improve infrastructure user safety.

A Novel Technique of Ranking of Transmission Lines for Maintenance Prioritization Accounting Annual Outage Time and Operational Severities

A Novel Technique of Ranking of Transmission Lines for Maintenance Prioritization Accounting Annual Outage Time and Operational Severities

Framework to assess the resilience of the motorway network in Austria

In the context of ageing of road infrastructure and increasing hazards due to climate change, it is important to allocate available funds for construction works and maintenance such that functionality loss is minimised in its extent and duration. In other words, the aim is to increase the resilience of infrastructure. In this paper, the outcomes of the project REMAIN (REsilient MotorwAy INfrastructure) are presented. Within REMAIN, a framework was developed to assess the resilience of the whole motorway infrastructure in Austria, considering five distinct asset categories: bridges, tunnels, noise barriers, retaining walls, and roadways. The resilience was assessed regarding 18 hazards of natural, technical, and human origin. The framework is designed to provide the motorway operator with a series of performance indicators solely based on resilience aspects. It is intended to constitute an additional decision-making basis for infrastructure maintenance prioritisation, next to already existing indicators, e.g., condition-based indicators. Emphasise was placed on making the framework executable also in cases of limited data availability, however, additional data can be integrated easily. The ability to predict the resilience of the motorway infrastructure of a whole country regarding all major hazards makes the presented framework unique regarding the comprehensiveness of applicability.

Data-driven model to predict burst pressure in the presence of interacting corrosion pits

This paper presents a data-driven approach to predict the pipelines’ corrosion-induced Burst failure. In this approach, different aspects of pit growth progression and spatial distribution of pits are simulated. The proposed approach takes advantage of population characteristics to model these aspects of the degradation paths for each pipe section down to the size of single joints. The insights obtained from simulations are used to project the degradation of each pipe section. Understanding corrosion behavior and field data are used to model the corrosion-related parameters such as corrosion pit dimensions, probability and time of initiation, and location. The failure is modeled using the probabilistic simulation considering degradation rate, interactions among pits, and material properties as stochastic variables. The proposed approach and included models are tested using multiple real-life inline inspection datasets. Validation of predicted properties shows prediction errors ranging from 3%–10% depending on the three remaining strength calculation approaches. This work aimed to serve as an important tool for risk-based maintenance prioritization, inspection interval assessment, and the fitness of service assessment of pipelines.

Prioritization of Egyptian Road Maintenance Using Analytic Hierarchy Process

Prioritization of Egyptian Road Maintenance Using Analytic Hierarchy Process

Automating intersection marking data collection and condition assessment at scale with an artificial intelligence-powered system

Intersection markings play a vital role in providing road users with guidance and information. The conditions of intersection markings will be gradually degrading due to vehicular traffic, rain, and/or snowplowing. Degraded markings can confuse drivers, leading to increased risk of traffic crashes. Timely obtaining high-quality information of intersection markings lays a foundation for making informed decisions in safety management and maintenance prioritization. However, current labor-intensive and high-cost data collection practices make it very challenging to gather intersection data on a large scale. This paper develops an automated system to intelligently detect intersection markings and to assess their degradation conditions with existing roadway Geographic information systems (GIS) data and aerial images. The system harnesses emerging artificial intelligence (AI) techniques such as deep learning and multi-task learning to enhance its robustness, accuracy, and computational efficiency. AI models were developed to detect lane-use arrows (85% mean average precision) and crosswalks (89% mean average precision) and to assess the degradation conditions of markings (91% overall accuracy for lane-use arrows and 83% for crosswalks). Data acquisition and computer vision modules developed were integrated and a graphical user interface (GUI) was built for the system. The proposed system can fully automate the processes of marking data collection and condition assessment on a large scale with almost zero cost and short processing time. The developed system has great potential to propel urban science forward by providing fundamental urban infrastructure data for analysis and decision-making across various critical areas such as data-driven safety management and prioritization of infrastructure maintenance.

Evaluating the Residential Outdoor Environment for the Elderly in Shuiqinggou, Qingdao, China using the Seniors’ Outdoor Survey Tool

As China’s elderly population grows, residential areas need to provide an outdoor environment for their physical and mental health in good condition. Many studies have found that better outdoor environments in residential areas can improve the health of the elderly and increase their happiness. The Seniors’ Outdoor Survey (SOS) tool was developed as an effective and reliable assessment tool to evaluate the needs and preferences of the elderly in outdoor spaces. This study used the SOS tool to evaluate Shuiqinggou Park in Qingdao, Shandong Province, China. The outdoor environment is divided into three parts for five domains of evaluation. The final average score of the whole environment is 84.35, the percentage is 86.9% compared with the total score, the Inter-rater ICC among trainers is 0.917, and the T-test value is 0.799. Through the results of the tools, it can be concluded that Shuiqinggou Park is a favorable outdoor space suitable and safe for the elderly. The study’s findings can help project managers and designers identify the essential outdoor characteristics for future construction and maintenance prioritization.

Adoption of prescriptive analytics for naval vessels risk-based maintenance:A conceptual framework

Prescriptive analytics is a novel maintenance approach by recommending the maintainer, the right maintenance action at the right time through prioritization of maintenance based on the risk. It assists in improving the existing preventive maintenance approach by offering a dynamic maintenance plan based on risk or Risk-based Maintenance Scheduling. To date, there is no clear definition of Naval Vessels Risk-based Maintenance as well as its framework, and it has not been applied extensively in naval vessel maintenance application which is peculiar compared to its application in plant maintenance. This paper reviews Risk-based Maintenance application in industries in an approach to develop prescriptive analytics for a comprehensive and dynamic naval vessels Risk-based Maintenance decision-making framework. It aims to establish an optimal maintenance scheduling to improve naval vessels availability and reduce maintenance costs while meeting stakeholders’ expectations. Objectives and considerations are also defined for peculiarities application of naval vessels. The framework is then evaluated to improve the effectiveness of the current Preventive Maintenance practice and the inconsistency of the existing maintenance decision-making model using the expert judgement in Reliability Centered Maintenance.

Maintenance of Roads Implemented under the Roads 2000 Strategy in Central Kenya

Road maintenance is an integral component of the low-volume sealed roads provision process, the type and cost of which are influenced significantly by decisions made during the preceding planning, design, and construction phases. Low-volume sealed roads are particularly sensitive to the whims of the physical environment so timely and effective control of their deterioration becomes the key challenge to the management of road maintenance. In Kenya, the Kenya Roads Board (KRB) holds the mandate for funding, oversight, and coordination of road maintenance, rehabilitation, and development through the optimum utilisation of resources for a sustainable road network. Each year, KRB finances the maintenance of the road network through the Kenya Roads Board Fund (KRBF). The Fund is distributed among the road agencies charged with maintenance. KRB reviews, individually, the Annual Road Works Programmes (ARWPs) submitted by Road Agencies and consolidate these ARWPs into an Annual Public Roads Programme (APRP). This study aimed at assessing the level and prioritisation of maintenance of the completed and handed-over projects under the Roads 2000 strategy. The condition of the carriageway was assessed, as well as the resilience and adaptation measures in place to respond to climate change. For climate resilience, data such as erosion, problematic soils, drainage from the road and its near environment as well as from outside the road reserve, instability of embankments and cuttings, construction issues and maintenance problems were collected using a standard form for rating against each data type. The assessment conducted on the performance of the side drainage showed that the majority of the roads have side drainage with inadequate depths and are not free-flowing. The high roughness values obtained, which were deduced to be as a result of distresses such as rutting and potholes, were concluded to be as a result of inadequate and untimely maintenance of the surface of the road. The recommended maintenance strategy should be such that interventions for the defects such as cracks, potholes, and edge breaks, among others are carried out as soon as they are identified. It was observed that all the roads surveyed did not have adequate adaptability measures for climate resilience. It was recommended that the Annual Road Inventory and Conditional Survey (ARICS) incorporates the collection of additional data in the assessment on issues that touch on climate resilience, the assessment of which should inform the implementation of appropriate adaptation techniques to improve the climate resilience of the completed roads, and more so the low volume sealed roads

Failure Conditions Assessment of Complex Water Systems Using Fuzzy Logic

Climate change, energy transition, population growth and other natural and anthropogenic impacts, combined with outdated (unfashionable) infrastructure, can force Dam and Reservoir Systems (DRS) operation outside of the design envelope (adverse operating conditions). Since there is no easy way to redesign or upgrade the existing DRSs to mitigate against all the potential failure situations, Digital Twins (DT) of DRSs are required to assess system’s performance under various what-if scenarios. The current state of practice in failure modelling is that failures (system’s not performing at the expected level or not at all) are randomly created and implemented in simulation models. That approach helps in identifying the riskiest parts (subsystems) of the DRS (risk-based approach), but does not consider hazards leading to failures, their occurrence probabilities or subsystem failure exposure. To overcome these drawbacks, this paper presents a more realistic failure scenario generator based on a causal approach. Here, the novel failure simulation approach utilizes fuzzy logic reasoning to create DRS failures based on hazard severity and subsystems’ reliability. Combined with the system dynamics (SD) model this general failure simulation tool is designed to be used with any DRS. The potential of the proposed method is demonstrated using the Pirot DRS case study in Serbia over a 10-year simulation period. Results show that even occasional hazards (as for more than 97% of the simulation there were no hazards), combined with outdated infrastructure can reduce DRS performance by 50%, which can help in identifying possible “hidden” failure risks and support system maintenance prioritization.

An integrated fuzzy-FMEA risk assessment approach for reinforced concrete structures in oil and gas industry

The oil and gas industry is one of the harshest environments on reinforced concrete structures. Enhancing the reliability of these industries has been identified as a critical goal to meet anticipated production targets and maintain competitiveness. The purpose of this paper is to rank and prioritize risk factors on reinforced concrete structural systems in the oil and gas industry to reduce failures and improve system reliability. The risk factors influencing reinforced concrete systems are identified based on the experts interviewed who specialized in risk analysis. In this paper, a risk assessment approach based on a hybrid fuzzy failure mode and effect analysis is developed in order to rank the factors and improve the process of reinforced concrete maintenance prioritization. The developed approach is also compared with the other two methods; namely, conventional failure mode and effect analysis (FMEA) and grey rational analysis (GRA) integrated with FMEA. The three developed approaches are designed to acquire the highest risk priority number (RPN) values; conventional RPN, GRA-FMEA RPN, and Fuzzy-FMEA RPN. These values will be utilized as the focus of improvements to reduce the possibility of some kind of failure occurring a second time and improve the deteriorated reinforced concrete structure to minimize the likelihood of failures. The results revealed that high-risk systems include the compression train, steam turbine, and combustion gas turbine generator, while the majority require maintenance of the supporting concrete foundation as soon as second-degree deterioration occurs. Furthermore, the results indicated that the Fuzzy FMEA approach was appropriate for assessing deteriorated reinforced concrete structures.. This work represents a step forward in the development of a tool that can be used to assess the risk of degraded concrete structures and improve their integrity through proper monitoring and maintenance.

Prioritising the Maintenance of University Hostels to Improve Students’ Satisfaction

The maintenance of university hostels is found to be unsatisfactory due to budget constraint in general. Hence, the purpose of this study is to find out a solution to improve the building maintenance and students’ satisfaction within the budget allocation. The development of maintenance prioritisation framework through the association between conditions of building components and students’ satisfaction is done to achieve the purpose of this research. Seven main building components were listed after a thorough literature review. Then, a total of 415 valid questionnaire responses were analysed to measure the conditions of building components and to establish the relationship between the conditions of building components and students’ satisfaction. Moreover, semi-structured interviews were carried out to validate and further interpret the survey findings. The research findings confirmed that utilities and floor as the significant building components to be prioritised in maintenance. It is worthwhile to be served as a guide to other university hostels. Furthermore, it opens a research opportunity to cover other institution hostels. This research is informative to the university hostel managements in efforts to improve the maintenance efficiency within limited budget. This study proposes a solution to tackle the maintenance issues in university hostels.

A fuzzy-logic-based method for maintenance prioritization of high-voltage SF6 circuit breakers, considering uneven wear

High-voltage circuit breakers are among the most critical elements of the switching nodes of the power system. The localization of failures and the restoration of the normal operational mode of the power system are largely determined by the reliable operation of circuit breakers when switching off short circuit currents. This paper presents a fuzzy logic-based method to estimate the reliability of high-voltage SF6 circuit breakers and to set priorities for the maintenance schedule. In this method a realistic mathematical model is combined with a fuzzy inference system, and its effectiveness was tested with a MATLAB-Simulink model. Based on the simulation results, a preventive maintenance strategy for high-voltage SF6 circuit breakers can be proposed for particular operational states and conditions.