Asset Lifecycle Management Research Articles

Multi-hazard life-cycle consequence analysis of deteriorating engineering systems

Probabilistic life-cycle consequence (LCCon) analysis (e.g., assessment of repair costs, downtime, or casualties over an asset’s service life) can enable optimal life-cycle management of critical assets under uncertainties. This can lead to effective risk-informed decision-making for future disaster management (i.e., risk mitigation and/or resilience-enhancing strategies/policies) implementation. Nevertheless, despite recent advances in understanding, modeling, and quantifying multiple-hazard (or multi-hazard) interactions, most available LCCon analytical formulations fail to accurately compute the exacerbated consequences which may stem from incomplete or absent repair actions between different interacting hazard events. This paper introduces a discrete-time, discrete-state Markovian framework for efficient multi-hazard LCCon analysis of deteriorating engineering systems (e.g., buildings, infrastructure components) that appropriately accounts for complex interactions between natural hazard events and their effects on a system’s performance. The Markovian assumption is used to model the probability of a system being in any performance level (i.e., limit state) after multiple hazard events inducing either instantaneous and/or gradual deterioration and after potential repair actions through implementing stochastic (transition) matrices. LCCon estimates are then obtained by combining limit state probabilties with suitable system-level consequence models in a computationally efficient manner. The proposed framework is illustrated for two case studies subject to earthquake and flood events as well as environment-induced corrosion during their service life. The first is a reinforced concrete building and the second is a simple transportation road network with a reinforced concrete bridge.

Advanced materials and deepwater asset life cycle management: A strategic approach for enhancing offshore oil and gas operations

Offshore oil and gas operations are inherently complex, requiring a strategic approach to asset life cycle management to ensure efficiency, safety, and environmental sustainability. This review explores the use of advanced materials in deepwater asset management, highlighting their role in enhancing operational performance and longevity. The review begins by discussing the challenges associated with deepwater operations, including harsh environmental conditions, high pressures, and corrosive fluids. These challenges necessitate the use of advanced materials that can withstand such conditions while maintaining structural integrity and operational efficiency. The review then outlines the strategic approach to asset life cycle management, emphasizing the importance of integrating advanced materials into design, construction, and maintenance processes. This approach includes the selection of materials based on their performance characteristics, compatibility with existing infrastructure, and cost-effectiveness. Furthermore, the review discusses the benefits of using advanced materials in deepwater asset management, including improved corrosion resistance, enhanced structural strength, and reduced maintenance requirements. These benefits translate into increased operational uptime, reduced downtime for repairs, and overall cost savings. Finally, the review concludes by highlighting the importance of collaboration between industry stakeholders, including operators, manufacturers, and research institutions, to drive innovation in advanced materials and deepwater asset management. By embracing a strategic approach and leveraging advanced materials, offshore oil and gas operations can achieve higher levels of efficiency, safety, and sustainability. Keywords: Advanced Materials, Deepwater Asset, Strategic Approach, Life Cycle Management, Oil and Gas Operations.

Intergenerational transfer of engineering expertise: knowledge continuity management in storm surge barrier engineering

PurposeEngineering knowledge continuity is crucial for the life cycle management of long-lived and complex assets, such as nuclear plants, locks and storm surge barriers. At the storm surge barriers in the Netherlands, engineering knowledge continuity is not yet fully assured, despite long-standing efforts. This study aims to explore the relationship between system characteristics, the organizational demarcation of maintenance and operation and the challenges in achieving engineering knowledge continuity and provides suggestions for improvement of theory and policy.Design/methodology/approachTen semi-structured interviews were conducted with professionals from various backgrounds in construction, engineering and asset management of the Dutch storm surge barriers, augmented with visits to barriers and barrier teams. A thematic analysis was used to identify and describe the challenges to engineering continuity, their origins and potential solutions. We reviewed knowledge management policy documents and asset management consultancy reports to validate the findings. Additionally, we engaged in frequent interactions with professionals at the barriers. We achieved saturation and validation once no new issues were raised during these discussions.FindingsThe thematic analysis developed multiple themes describing the challenges to engineering continuity, their origins and potential solutions. The key findings are that expert engineers are critically important to deal with redesigns induced by obsolescence. Moreover, due to barrier uniqueness, long redesign cycles and reliability requirements, conventional knowledge continuity tools are insufficient to enable new engineers to reach expert level. Finally, the thematic analysis shows that, in some cases, outsourcing should be reduced to facilitate internal learning.Originality/valueThe study introduces the application of the knowledge-based view of the firm and the concept of requisite knowledge redundancy to the long-term management of complex assets. It calls for more attention to long gaps in the use of unique knowledge and the effect on knowledge continuity.

Digital transformation maturity assessment framework for large infrastructure asset owners

This research project developed a framework for assessing digital transformation (DT) maturity in large government-owned corporations (GOCs), focusing on integrating building information modelling (BIM), digital engineering (DE) practices, value of information, data integration aspects, and critical information management competencies, relating to asset management, asset delivery, and asset performance. Recognising DT’s strategic importance in asset lifecycle management, researchers created a practical DT maturity assessment tool applied to an Australian GOC. The framework and Excel assessment tool outline DT maturity across seven categories and 56 indicators, using the analytical hierarchy process (AHP) to rank indicators based on expert input. The tool enables straightforward data input and DT maturity evaluation, helping organisations understand their current DT status and set improvement targets. Workshops with DT professionals validated the tool’s effectiveness through a case study, offering insights into DT maturity levels and a roadmap for technological integration and data-driven decision-making. The research highlights DT’s role in enhancing efficiency, innovation, and safety, emphasising the need for strategic alignment with organisational objectives.

Improving Foundational Understanding regarding Facility Transition through a Survey Assessment of Industry Professionals involved in Commercial Building Decommissioning

ABSTRACT Commercial buildings are asset intensive facilities and therefore have unique challenges in the asset lifecycle management. When procedures and guidelines on decommissioning are not enforced, these buildings can be left unattended for years. As a result, the lack of maintenance may expedite asset deterioration, thus leaving less material salvageable for future use. This study aims to better understand current trends in the decommissioning of commercial buildings by surveying industry professionals across three possible dimensions 1) planned decommissioning, 2) condition assessments on commercial buildings, and 3) current awareness of procedures and guidelines. Through correlation analysis, the study explores how professionals’ roles in the industry relate to these three dimensions, offering valuable insights into the dynamic interplay shaping decommissioning practices. This study analyzes data collected through a Qualtrics survey designed to target construction and facilities management professionals. The 720 respondents were associated with the International Facilities Management Association and the American Institute of Architects. Results of the study show that planning for decommissioning mostly occurs once a building has been vacated or due to reactionary planning in an attempt to save a facility for reuse or remodeling. There is a consensus among the survey respondents that condition assessments on unoccupied/vacated buildings should be conducted annually. Results also indicate that although there are instances where vacated facilities maintained a minimal supply of utilities, there are many instances where facilities have had lights switched off, doors locked, and the building completely shut down. This study provides results that identify key variables as to what may influence how a building is decommissioned and promote standardized decommissioning procedures and guidelines for commercial buildings.

Assessing the prospects of distributed ledger technology (DLT) development as an element of digital ecosystem of railway transport

Purpose: to evaluate the practice of implementing projects for digitalization of the life cycle management processes of railway infrastructure facilities based on distributed ledger technologies (DLT). Methods: general scientific methods such as observation, analysis, comparison, generalization and analogy were used. As a result of the conducted research: the main characteristics of the distributed ledger technologies, which determine the parameters and scope of their use in life cycle management of railway transport facilities, have been systematized; the effects of the implementation DLT as an element of the digital railway transport ecosystem have been identified; recommendations for the implementation and rollout of design solutions using DLT in the railway transport area have been developed. Conclusion: as a result of the study, recommendations for the implementation and rollout of the solutions using DLT have been developed. The findings and conclusions may be useful for railway companies, government agencies and other participants in the railway transport and logistics market when planning and implementing projects for optimizing asset life cycle management.

Deriving inferences through natural language from structured datasets for asset lifecycle management

Industry 4.0 is expected to revolutionize the manufacturing world in terms of improved decision-making leading to increased value extraction from assets. Yet, since its inception, its adoption seems to have not scaled with time as expected. One of the bottlenecks can be due to the knowledge disparity between the Industry 4.0 technology developer and the domain expert/end user, which can lead to higher resources, cost, and time requirements for scalable adoption. Modern day Large Language Models (LLMs) have the ability to process large datasets and can serve as an intermediatory tool for domain end-users. Thus, in this paper, an Industrial-GPT tool is developed to translate natural language queries into meaningful inferences about asset-related data. The tool is evaluated using an exemplary dataset, representing a manufacturing industry with four production lines, multiple assets, readings and inferences from multiple sensors. The experimental results show that reasonable insight may be extracted from the dataset, using prompt engineering, while fine-tuning the model to adapt to business logic can help achieve better and faster inferences. Future research directions are proposed that can be addressed while developing Industrial-GPT-based tools and reducing the development cycle for Industry 4.0 technologies.

Preliminary investigation of the state of the art on digital servitization for Industrial Asset lifecycle Management

This paper aims to investigate how digital servitization can act as a game changer of Industrial Asset lifecycle Management in manufacturing. Building on a literature review, two main research streams are identified when talking about digital servitization for asset management. A part of the analyzed papers reports the Product-Service Systems as the solutions developed by Original Equipment Manufacturers for managing users’ industrial assets; this primarily focuses on the required change of the value proposition in the offerings due to business models. These papers address a few asset-related processes like the real-time status/process monitoring, or the prognosis and diagnosis of failures. A higher collection of the identified papers addresses the opportunities brought by digitalization to the users of the assets for developing digital services; this involves more actors, and leverages both data-sharing and collaboration. A good portion of this research stream considers the digital capabilities from advanced platforms enabled by infrastructures as Cyber-Physical Systems and Digital Twin models for developing smart services solutions. The digital platforms, open and interoperable, appear to be a promising means where to start from in order to develop the collaborative approaches required by Industrial Asset lifecycle Management; correspondingly, a wider set of asset-related processes can be supported.

A life-cycle asset management model by response surface method based optimization

Conventional asset management is easily trapped in local optimization. This study integrates the maintenance strategic planning in the asset management scheme to minimize the annualized Total Cost of Ownership (TCO). Critical steps are considered in the proposed model including failure rate modeling, reliability modeling for preventive maintenance, and TCO modeling. Monte Carlo simulation and response surface methodology are used as an integrated solution approach for the proposed model. The case study in a cement manufacturing plant shows that the model delivers the desired optimization result. Sensitivity analysis is carried out in terms of equipment contribution, clinker price, and escalation rate. The developed model contributes to the optimum combination of preventive maintenance strategy and useful life which provides the minimum annualized TCO.

BIM enabler for facilities management: a review of 33 cases

The purpose of this paper is to investigate the application of building information modelling (BIM) for asset management. It will discuss what tools and processors are used for applying BIM for asset management in construction projects. The method comprises a literature review of 33 projects that have applied BIM or planned to apply BIM for asset management functions. The findings show that a combination of software, hardware, processors, and standards can enable the BIM models to be integrated with the asset management systems. It facilitates easy extraction of information, avoid errors and duplication of work. The most popular way of developing a BIM-based asset management system is incorporating BIM asset data with CMMS or CAFM systems used for asset lifecycle and maintenance management. Engagement of stakeholders related to the operation phase is required as early as possible. Consideration should be given for protecting the information generated within the cyber environment. The research shows how BIM related tools and processors could be integrated to successfully implement BIM enabled asset management. The knowledge can be potentially useful for planning or implementing BIM enabled asset management systems. The research provides insight on the application of BIM for asset management in construction projects globally.

Industry 4.0 Technologies for Sustainable Asset Life Cycle Management

Asset life cycle management is not a new concept for industries [...]

ADVANCING OFFSHORE OIL AND GAS FACILITIES: A COMPREHENSIVE REVIEW OF INNOVATIVE MAINTENANCE STRATEGIES FOR ENHANCED RELIABILITY AND EFFICIENCY

The offshore oil and gas industry plays a crucial role in meeting global energy demands by providing reliable and efficient operations for offshore facilities. This comprehensive review explores various innovative maintenance strategies aimed at enhancing reliability and efficiency in the industry. The article emphasizes the significance of maintenance for offshore operations and delves into different innovative strategies. Condition-Based Maintenance (CBM) techniques enable proactive monitoring of equipment health, optimizing maintenance schedules, and reducing unscheduled downtime. Predictive maintenance, powered by data analytics and machine learning, allows for the prediction of potential failures and prioritization of maintenance activities. Prognostics and Health Management (PHM) systems facilitate early fault detection and preventive actions, enhancing asset reliability. Reliability-Centered Maintenance (RCM) approaches focus maintenance efforts on critical assets, ensuring efficient resource allocation. The integration of robotics and automation revolutionizes inspection, repair, and maintenance activities, reducing human intervention and enhancing safety. Digital twins, virtual replicas of physical assets, enable simulation and optimization of maintenance processes. Despite the immense potential of these innovative maintenance strategies, implementation faces challenges such as technological complexity, economic considerations, and regulatory compliance. Looking ahead, the industry’s focus on sustainability and the global energy transition will drive maintenance practices that minimize environmental impact. Renewable energy integration, emission reduction efforts, and sustainable asset lifecycle management will shape future maintenance approaches. Embracing digital transformation and investing in workforce training are crucial for unlocking the full potential of innovative maintenance strategies. In conclusion, this comprehensive review highlights the diverse landscape of innovative maintenance strategies available to offshore oil and gas facilities. By embracing these transformative approaches, operators can elevate reliability, efficiency, and safety, ensuring sustainable operations amid dynamic industry demands.

Trends in Adopting Industry 4.0 for Asset Life Cycle Management for Sustainability: A Keyword Co-Occurrence Network Review and Analysis

With the potential of Industry 4.0 technologies to enable sustainable manufacturing, asset life cycle management (ALCM) has been gaining increasing attention in recent years. This study explores the evolution of Industry 4.0 technology applications to sustainable ALCM from 2002 to 2021. This study is based on keywords collected from 3896 ALCM-related scientific articles published in the Web of Science, IEEE Xplore and Engineering Village between 2002 and 2021. We conducted a review analysis of these keywords using a network science-based methodology, which unlike the tedious traditional literature review methods, gives the capability to analyze a huge number of scientific articles efficiently. We built keyword co-occurrence networks (KCNs) from the keywords and explored the network characteristics to uncover meaningful knowledge patterns, knowledge components, knowledge structure, and research trends in the body of literature at the intersection of ALCM and Industry 4.0. The network modeling and data analysis results identify the emerging Industry 4.0-related keywords in ALCM literature and indicate the recent explosion of connectivity among keywords. We found IoT, predictive maintenance and big data to be the top three most popular Industry 4.0-related keywords in ALCM literature. Furthermore, this study maps relevant ALCM keywords in contemporary literature to the nine pillars of Industry 4.0 to help the responsible manufacturing community identify research trends and emerging technologies for sustainability.

Engineering Brain: Metaverse for future engineering

The past decade has witnessed a notable transformation in the Architecture, Engineering and Construction (AEC) industry, with efforts made both in the academia and industry to facilitate improvement of efficiency, safety and sustainability in civil projects. Such advances have greatly contributed to a higher level of automation in the lifecycle management of civil assets within a digitalised environment. To integrate all the achievements delivered so far and further step up their progress, this study proposes a novel theory, Engineering Brain, by effectively adopting the Metaverse concept in the field of civil engineering. Specifically, the evolution of the Metaverse and its key supporting technologies are first reviewed; then, the Engineering Brain theory is presented, including its theoretical background, key components and their inter-connections. Outlooks of this theory’s implementation within the AEC sector are offered, as a description of the Metaverse of future engineering. Through a comparison between the proposed Engineering Brain theory and the Metaverse, their relationships are illustrated; and how Engineering Brain may function as the Metaverse for future engineering is further explored. Providing an innovative insight into the future engineering sector, this study can potentially guide the entire industry towards its new era based on the Metaverse environment.

Discussion on Information Construction Strategy of Asset Lifecycle Management in Power Grid Enterprises

Discussion on Information Construction Strategy of Asset Lifecycle Management in Power Grid Enterprises

A Study on The Intention to Use Total Cost of Ownership System in Tenaga Nasional Berhad

This research aims to discover factors that influence behavioural intention among Tenaga Nasional Berhad (TNB) Grid employee to adopt Total Cost of Ownership (TCO) system as a new technology in daily operation. TCO system is expected to improve and optimize OPEX of TNB Grid. This research adopted Technology Acceptance Model (TAM) by Davis et al. (1989) as the framework of the study. Despite utilising perceived usefulness and perceived ease of use as the dimension, this research study extends the investigation by incorporating perceived risks as per model by Khasawneh (2015). This research study is based on the data collected from a survey questionnaire that investigates factors that influence behavioural intention to accept TCO system among TNB Grid employee. The quantitative method is used to gather the information in the non-contrived study setting to provide a natural environment of the TNB Grid. The stratified random sampling method is used whereby the target population; employees dealing with the entire asset lifecycle management of high voltage equipment, is divided into meaningful segments such as job’s position, office location, department and years of service. The research discovered there are strong relationship between perceived usefulness, perceived ease of use and perceived risks with the behavioural intention to adopt TCO system among TNB Grid employees. The research also highlighted the important element to be considered by TNB in developing the efficient TCO system which is the ease-of-use system. The positive attitude will drive behavioural intention to use the system if the user perceives ease of use.

MetaAnalyser - A Concept and Toolkit for Enablement of Digital Twin

Digital Twin (DT) has promising impact on the life cycle management of assets in manufacturing industry. The concept of DT has become possible with digitalisation and Artificial Intelligence (AI). Data driven Machine Learning (ML) capabilities, can enhance the performance of the DT. To replicate a dynamic system, the DT should continuously receive and process incoming data in real-time. However, every time that the system receives new incoming datasets, the challenges of ML such as data preparation, feature selection, model selection and performance evaluation, slow down the development process of DT. This paper proposes a MetaAnalyser platform that automates these steps for incoming datasets in real-time. The MetaAnalyser platform through automating data preparation, feature selection, model selection and performance evaluation, is expected to increase the level of agility in the development process of DT and the efficiency of the DT during its lifecycle. The MetaAnalyser platform is demonstrated in this paper by ranking the features that affect the arrival delays in trains and ranking regression models based on their performance on the dataset.

Assessing the Maturity of the Asset Lifecycle Management in the Case of the Massachusetts Bay Transportation Authority (MBTA)

Asset management is a central capability that organizations have to perform well. Asset management is concerned about the management of assets that are valuable or potentially valuable to an organization. This article focuses on asset management as it is performed in the railway transit industry. The past decade has seen a number of positive changes in the way that transit agencies manage their assets. While many transit agencies have introduced asset management approaches, work still needs to be done in the area of how we assess the maturity of asset management programs. This article proposes a framework for assessing the maturity of asset management programs, especially those that are used to manage individual assets according to their lifecycles. To illustrate the value of the proposed asset management maturity framework, we describe the asset management transformation at a transit agency and use the proposed framework to document the gains of the improvement effort.

Future of photovoltaic technologies: A comprehensive review

Presently, the world is going through a euphoric rush to install photovoltaic (PV) devices in deserts, over water bodies, on rooftops of houses, vehicles, and parking spaces, and many other applications. The cumulative PV installation is estimated to have crossed 600 GW globally to date and is expected to cross 4500 GW by 2050 due to sustained investment and continual innovation in technology, project financing, and execution. This article presents a critical and comprehensive review of the wide spectrum of present and future PV technologies, not only in terms of their performance but also in terms of the aspects of their end-of-life waste management and ecotoxicity, which have been largely neglected by the researchers and policymakers. The global status of the regulatory framework is reviewed as well, with regard to the life cycle management of PV waste. And It is found that presently, the world is very poorly equipped with regulatory frameworks to deal with massive PV waste (about 78 million tonnes), expected to be generated by 2050. Based on the findings, an immediate and disruptive paradigm shift is proposed in the policy framework, from the promotion of new PV installation to life cycle management of PV assets.

BIM for Smart Hospital Management during COVID-19 Using MCDM

In context of the recent COVID-19 pandemic, smart hospitals’ contributions to pre-medical, remote diagnosis, and social distancing has been further vetted. Smart hospital management evolves with new technology and knowledge management, which needs an evaluation system to prioritize its associated criteria and sub-criteria. The global effect of the COVID-19 pandemic further necessitates a comprehensive research of smart hospital management. This paper will utilize Analytical Hierarchy Process (AHP) within Multiple Criteria Decision Making (MCDM) to establish a smart hospital evaluation system with evaluation criteria and sub-criteria, which were then further prioritized and mapped to BIM-related alternatives to inform asset information management (AIM) practices. This context of this study included the expert opinions of six professionals in the smart hospital field and collected 113 responses from hospital-related personnel. The results indicated that functionalities connected to end users are critical, in particular IoT’s Network Core Functionalities, AI’s Deep Learning and CPS’s Special Network Technologies. Furthermore, BIM’s capability to contribute to the lifecycle management of assets can relate and contribute to the asset-intensive physical criteria of smart hospitals, in particular IoT, service technology innovations and their sub-criteria.