Advances in Asset Management: Strategies, Technologies, and Industry Applications
- 2024
- Book
- Editors
- Adolfo Crespo Márquez
- Turuna S. Seecharan
- Georges Abdul-Nour
- Joe Amadi-Echendu
- Book Series
- Engineering Asset Management Review
- Publisher
- Springer Nature Switzerland
About this book
This book discusses asset life-cycle management, especially, human dimensions on the management of infrastructure and industry-sector assets.
The book explores advances decision support systems based on the applications of Fourth Industrial Revolution (4IR) technologies such as augmented reality (AR) and virtual reality (VR), machine learning, and digital twinning for monitoring, diagnostics, prognostics. It includes methodologies and cases applied to different operational contexts.
The book also considers the implications of the applications of international standards, local regulations and industry guidelines to risk and resilience engineering asset operations.
Advertisement
Table of Contents
-
Frontmatter
-
Risk Management and Qualitative Analysis
-
Frontmatter
-
RQCM: Risk Qualitative Criticality Matrix. Case Study: Ophthalmic Lens Production Systems in Costa Rica
Carlos Parra, Juan Rodríguez, Adolfo Crespo Márquez, Vicente González-Prida, Pablo Viveros, Fredy Kristjanpoller, Jorge ParraThe chapter delves into the Risk Qualitative Criticality Matrix (RQCM) methodology, using a case study of ophthalmic lens production systems in Costa Rica. It begins by referencing the 8-phase Maintenance Management Model (MMM) and focuses on Phase 2, which involves criticality analysis techniques. These techniques help identify and prioritize maintenance tasks based on the risk level associated with not performing them. The chapter discusses various criteria for evaluating asset criticality, such as operational flexibility, impact on production capacity, and health, safety, and environmental factors. It introduces the Qualitative Risk Matrix (QRM) as a precursor to the RQCM, highlighting the importance of clear event identification and risk assessment. The case study demonstrates how the RQCM can be applied to determine the criticality of production systems, providing a structured approach to risk management and maintenance prioritization.AI Generated
This summary of the content was generated with the help of AI.
AbstractThe use of prioritization analysis techniques allows identifying the level of criticality of physical assets and helps to manage resources: human, economic and technological in a more efficient way. In other words, the process of criticality analysis helps to determine the importance and consequences of the failures of productive equipment in the operational context in which they perform. This article explains the basic theoretical aspects of the equipment prioritization analysis process based on risk matrices (failure frequency and consequences); and the development of the model named Risk Qualitative Criticality Matrix (RQCM). Finally, are presented and analysed the results of a case of application of the RQCM in the sector of ophthalmic lenses (new factory built in Costa Rica – PRATS Laboratory). -
Factors Affecting the Quality of Network Services in Emerging Telecoms Operating Environment and Markets
Charles Okeyia, Nuno Marques AlmeidaThe chapter delves into the critical factors affecting the quality of network services in emerging telecom markets, emphasising the need for intelligent and digitalised asset management and maintenance strategies. It discusses the challenges posed by power availability, the impact of human and environmental factors, and the limitations of current reactive maintenance practices. The authors argue for the adoption of predictive-based maintenance strategies to enhance network quality of service and asset performance. The chapter also explores the integration of AI and human-centric approaches to optimise maintenance planning and execution, offering a comprehensive overview of the current state and future directions in this domain.AI Generated
This summary of the content was generated with the help of AI.
AbstractAs an emerging market, the telecoms sector in Nigeria has undergone a considerable increase in teledensity, internet usage and consumer base over a decade and is still on exponential growth. However, the consequence of this increase in growth has been a continuous degradation of telecom network quality of service (QoS), which has impacted subscribers’ customers’ needs, satisfaction, expectations and added value services. In exploring the quality of services (QoS) issues, the asset performance is not meeting the agreed key performance indicators (KPIs) on power availability (PA), a critical KPI which is affected by asset maintenance activities. Therefore, this paper focuses on the technical and human factors of asset management and maintenance practices. The methodology used in this paper is the quantitative and qualitative approaches with a systematic review of related literature on the research context. The primary data sources are through a structured survey questionnaire and semi-structured interviews. The secondary data source is the systematic literature review on related journal articles to the research subject matter. The paper used the statistical package for the social sciences software (SPSS 29) and Nvivo software for the data analysis. The research results and findings indicate critical maintenance strategic differences in existing asset maintenance activities and operations, cost pressure, and complex operating environments and markets that could be explained through intelligent and digitalised asset management and maintenance strategies. The systematic review results indicate the advancement of asset maintenance strategies to support maintenance planning, asset real-time monitoring and management, as the existing maintenance practice did not match the intelligent-based approach drawn from the concept of Industry 4.0R.
-
-
Technology and Innovation in Asset Management
-
Frontmatter
-
A Conceptual Implementation Process for Smart Maintenance Technologies
San Giliyana, Antti Salonen, Marcus BengtssonThis chapter explores the integration of smart maintenance technologies in Industry 4.0, focusing on the nine key technologies and the role of AI and CPS. It introduces a conceptual implementation process for these technologies, addressing challenges such as data quality, competence, and cross-functional collaboration. The process is validated through case studies from leading manufacturing companies, offering insights into both the benefits and obstacles of adopting smart maintenance practices. The chapter concludes with a call for further testing and refinement of the implementation process to better support the manufacturing industry.AI Generated
This summary of the content was generated with the help of AI.
AbstractIndustry 4.0 is usually presented as usage of technologies. Some of these play an important role in the development of smart maintenance technologies. However, although the subject of smart maintenance has been discussed for more than 10 years, the manufacturing industry still finds it challenging to implement smart maintenance technologies to add benefits to maintenance organizations in line with company’s goals. This study presents a conceptual process for implementing smart maintenance technologies, challenges and enablers to consider when implementing, and benefits. This article is based on an analysis of empirical findings from seven large manufacturing companies in Sweden, previous maintenance research, and authors’ three previous smart maintenance research articles. In the first article, the authors explored perspectives on smart maintenance technologies from 11 large companies within the manufacturing industry, while in the second one, perspectives on smart maintenance technologies from 15 manufacturing Small and medium-sized enterprises (SMEs) were presented. In the third and final one, the authors developed and presented a testbed for smart maintenance technologies. -
A Framework for Assessing Emerging Technology Risks in Industrial Asset
Issa Diop, Georges Abdul-Nour, Dragan KomljenovicThe chapter introduces a novel framework for assessing emerging technology risks in industrial assets, addressing the challenges posed by the complexity of socio-technical systems driven by Industry 4.0. Traditional risk analysis methods, while valuable, are insufficient to manage the interconnected and dynamic nature of modern systems. The proposed framework combines Functional Resonance Analysis Method (FRAM) and System-Theoretic Accident Model and Processes (STAMP) to provide a more comprehensive understanding of system interactions and risks. FRAM emphasizes the variability of functions within the system, while STAMP offers a top-down approach to safety analysis. The integration of these methods enables proactive identification and management of risks, enhancing system resilience and safety. The chapter also discusses the integration of this framework with the Risk-Informed Decision Making (RIDM) model, further enhancing the decision-making process. A case study on Hydro-Quebec’s LineDrone UAV is presented to illustrate the practical application of the framework. This chapter is a must-read for professionals seeking to advance their understanding of risk management in complex industrial settings.AI Generated
This summary of the content was generated with the help of AI.
AbstractThe management of risks in the context of Industry 4.0 is currently lacking accurate and efficient systematic approaches and tools, leading to a potential underestimation or unrealistic perception of risks in various domains where effective risk management is crucial. Traditional methods, while valuable, have limitations and may not adequately capture all the factors that influence system safety. To address the challenges posed by conventional industry issues, emerging risks, and the complexities of socio-technical systems, there is a need for comprehensive Asset Management and Decision Support approaches. These approaches should encompass both conventional and emerging risk safety management, providing innovative and efficient solutions to support practitioners in navigating these complex environments. Based on the rationale provided, this paper is dedicated to the identification and analysis of risk management components, particularly pertaining to emerging safety risks in the context of Industry 4.0. It also examines the challenges posed by extreme, rare, and disruptive events that have the potential to severely impact organizational performance. The research focuses on relatively new methods grounded in system theories, specifically the Functional Resonance Analysis Method (FRAM) and the System-Theoretic Accident Model and Processes (STAMP). These approaches are considered the most suitable for investigating and addressing the research objectives. To validate the efficiency and practicality of the adopted methods, further research initiatives will be focused on conducting case studies. These case studies will aim to gather more accurate data and insights related to the application of FRAM and STAMP in real-world scenarios.
-
-
Asset Health and Maintenance Strategies
-
Frontmatter
-
Challenges on an Asset Health Index Calculation
Eduardo Candón Fernández, Adolfo Crespo Márquez, Antonio Jesús Guillén LópezThe chapter delves into the intricacies of calculating an Asset Health Index (AHI), a vital tool for assessing and managing the health of complex assets. It begins by defining the AHI and its importance in reflecting an asset's condition and performance. The text then outlines the six-step methodology for calculating the AHI, highlighting the consideration of load and location factors, aging rate, and health and reliability modifiers. A case study involving motor pumps in a power generation plant is presented to illustrate the practical application of the methodology. The chapter also emphasizes the importance of validating AHI results through expert assessment to ensure accuracy and reliability. Throughout, the text provides a detailed and systematic approach to AHI calculation, making it an invaluable resource for professionals seeking to optimize asset management strategies.AI Generated
This summary of the content was generated with the help of AI.
AbstractIn the current era of Industry 4.0, we find ourselves in the midst of a profound transformation in the industrial landscape. This new era brings with it a host of challenges and problems, particularly in relation to the effective capture and processing of data. The success of this revolution hinges on our ability to harness data in a meaningful way, but achieving this goal is no small feat.At the core of this data-driven revolution lies the critical importance of capturing data accurately. However, in many companies, this proves to be an incredibly complex problem. It is not simply a matter of capturing as much data as possible from the moment an asset or system is initiated. Rather, the focus is on acquiring a minimum amount of data that is sufficient to enable proper processing and analysis. This requirement presents a unique challenge in itself, as it often necessitates estimating this minimum data requirement based on a solid and reliable foundation of existing information.The consequences of lacking adequate information can be far-reaching. Insufficient data availability inevitably leads to deviations in the processing and analysis of the captured data. However, this limitation also offers an opportunity for comparison. By examining assets of the same type that face similar challenges in data capture and processing, valuable insights can be gained. For instance, consider the scenario of comparing the health index of multiple transformers located in different electrical substations and operating under diverse conditions. If the data capture relating to the operational and maintenance variables is equally deficient across these transformers, and similar estimation techniques are employed, it becomes possible to compare the overall health of these equipment units.To delve deeper into this topic, let us explore the specific example of calculating the Health Index for different pumps. In this particular case, the challenge arises from the fact that the start-up of these pumps predates the availability of operation and maintenance data. Consequently, due to this lack of information, a different approach must be taken. The estimation of various fundamental variables becomes necessary to facilitate the calculation of the Health Index and derive meaningful insights into the condition and performance of the pumps.In conclusion, the advent of Industry 4.0 has brought forth a range of challenges and problems in the realm of data capture and processing. The ability to obtain and process data accurately is a critical factor in the success of this revolution. However, the complexity of the task lies not only in capturing a substantial amount of data but also in determining the minimum data requirements for meaningful analysis. Despite the difficulties posed by limited information, the comparison of similar assets facing data capture challenges can provide valuable insights. Through a specific example involving pump health index calculations, we can further understand the importance of addressing data estimation and processing in the context of Industry 4.0. Throughout this paper, the example of calculating the Health Index of different pumps will be developed in which the start-up of these goes back to times prior to the date of capture of the operation and maintenance data. Due to this lack of information, it will be necessary to start from the estimation of different fundamental variables for the processing of the data to be calculated. -
General Bases to Hierarchy Definition for Digital Assets in Railway Context
Mauricio Rodríguez, Adolfo Crespo Márquez, Antonio Jesús Guillén López, Eduardo Candón FernándezThe chapter introduces a robust hierarchical framework for railway assets, addressing the need for standardization in the digitalization of the railways industry. It integrates real-world, digital, and management dimensions to provide a comprehensive approach. The research identifies challenges such as selecting appropriate Maintenance Management models and aligning diverse digital solutions. The study also highlights the importance of a systemic perspective and the development of digital twins for effective asset management. The proposed framework aims to enhance efficiency, reduce downtime, and position railways competitively in the Industry 4.0 era. The research methodology involves a thorough literature review, consultation with infrastructure managers, and practical application in a European railway system, making it a valuable resource for professionals seeking to advance digital transformation in the railways industry.AI Generated
This summary of the content was generated with the help of AI.
AbstractDefining the existence of a digital asset, integrating multiple platforms that represent its entities digitally, and simultaneously meeting the specific demands of the operational context of railway infrastructure systems represents an unresolved challenge for this industry. This study focuses on the search for commonalities, complementing the perspectives of the scientific community and research centers with real-world applications. From there, the development of a framework presented in our research emerges, capturing both the state of the art and practice, providing a starting point for the development of scientific discussions and the search for future models that offer an effective solution to the problem. The integration of maintenance management models with architectures for the development of digital twins in Industry 4.0, and the applied study of the railway industry itself, are part of the foundation of this study. Seeking to adhere to the principles already proposed for Industry 4.0, the scheme introduces new relationship factors that will be prototyped in the industry, especially in railway infrastructures, allowing for scalability and the digitization of processes as crucial as the criticality assessment for asset prioritization. -
Determination of the Exact Economic Time for the Component Replacement Using Condition-Based Maintenance
Antonio Sánchez-Herguedas, Antonio Jesús Guillén-López, Francisco Rodrigo-MuñozThe chapter presents a detailed methodology for determining the optimal economic time for component replacement using condition-based maintenance (CBM). It introduces a semi-Markovian model to calculate the preventive interval and degradation threshold, considering various factors such as income, costs, and failure probabilities. The model is designed to optimize the expected accumulated return over time, providing maintenance managers with a powerful tool to enhance asset reliability and reduce maintenance costs. The chapter also discusses the background and applications of similar models, highlighting the unique contributions of the proposed methodology in the field of prognostics and health management (PHM).AI Generated
This summary of the content was generated with the help of AI.
AbstractIn most industrial assets, determining the preventive interval is a task carried out by the maintenance engineer. In non-critical assets, the optimization process of the interval must consider the costs of operation and maintenance, as well as the income generated by its operation. The result is the economic determination optimal moment to perform preventive intervention (PM). Mathematically, an expression can be found that relates these variables to the failure occurrence process. However, when the equipment is critical to the business, it is necessary to avoid the occurrence of failure. For this purpose, investment is made in techniques that determine asset degradation (CBM). In this case, not only must the failure occurrence process be controlled, but the degradation of the asset must also be analyzed. To determine the economically optimal moment for the preventive replacement of a component subject to CBM, a semi-Markovian model has been developed. The model considers degradation as a Wiener process and integrates it with the failure occurrence process, adjusted to a Weibull distribution. The result is two mathematical formulas to determine the optimal degradation threshold and the interval for preventive replacement, optimizing costs, income, degradation, and failure distribution.
-
-
Industry-Specific Asset Management and Other Considerations
-
Frontmatter
-
Audit Models for Asset Management, Maintenance and Reliability Processes: A Case Study Applied to the Desalination Plant
Pablo Duque, Carlos Parra, Félix Pizarro, Andrés Aránguiz, Emanuel VegaThe chapter focuses on the application of two audit models, AMORMS and AMS-ISO 55001, to evaluate the maintenance management processes of a desalination plant. It highlights the importance of these audits in identifying gaps and proposing action plans to enhance maintenance practices. The study provides a detailed analysis of the audit results, including radar charts and maturity scales, and offers specific recommendations for improving processes such as asset management planning, workshop management, and personnel development. The chapter concludes with recommendations for future work, emphasizing the need for continuous training and knowledge dissemination to achieve world-class performance in maintenance processes.AI Generated
This summary of the content was generated with the help of AI.
AbstractCurrently, the timely identification of improvements, shortcomings, and potential failures applied to maintenance has taken relevant attention from the scientific community in recent years. In order to carry out appropriate diagnosis, the employment of methods to properly measure the reliability of industrial processes has been a trend. In this work, AMORMS and AMS-ISO 55001 are applied to a seawater desalination plant aiming for carrying out a fitted measurement, generating suited improvement plans. In this context, AMORMS is a model based on 8 phases, which focuses on assets management. On the other hand, AMS-ISO 55001 focuses on the asset management norm ISO55001. The results yielded include the design and generation of actions to tackle the 20% more deficient categories needed to achieve a competitive industrial performance. -
Audit Model for Asset Management, Maintenance and Reliability Processes: A Case Study Applied to Pulp Mill Sector
Andrés Aránguiz, Félix Pizarro, Carlos Parra, Pablo Duque, Emanuel VegaThis chapter focuses on the application of an audit model for asset management, maintenance, and reliability processes within the pulp mill sector. It analyzes a leading company in the forestry sector with a Kraft pulp plant in Chile, aiming to optimize operational risk management and strategic decision-making in asset management. The audit, conducted using the AMORMS tool, evaluates eight phases of the maintenance management model, revealing significant gaps and areas for improvement. The results highlight the need for better maintenance practices, risk management, and continuous improvement programs to enhance the plant's efficiency and reliability.AI Generated
This summary of the content was generated with the help of AI.
AbstractCurrently, the optimization process in the maintenance management has been treated as a critical issue by the industry. The proposed work focuses on maintenance model diagnosis, the process aims to detect positive practices and highly possible future improvements in the models. In order to carry out the diagnostic, a systematic process is performed over the maintenance model employed through the usage of AMORMS (Asset Management, Operational Reliability & Maintenance Survey). The study case presented in this work was carried out over a pulp mill from Chile, which has an annual production over 1 million tons. Regarding the overall analysis output, several issues were illustrated in order to reach a world level performance. Thus, the employment of such instruments aims to detect key issues in urgent need to be fixed, helping in successfully designing a fitted model to be competitive and reach higher productivity. -
The Role of Eco-Driving and Wearable Sensors in Industry 4.0
Turuna S. SeecharanThe chapter delves into the critical role of eco-driving in enhancing road safety and reducing fuel consumption in Industry 4.0. It highlights the impact of aggressive driving habits on vehicle maintenance and fleet costs, emphasizing the need for smoother driving behaviors. The study investigates the relationship between drivers' emotional arousal, measured using Electrodermal Activity (EDA) sensors, and their eco-driving scores. By analyzing real-time data from wearable sensors and telematic devices, the research aims to understand how emotional states influence driving performance. The methodology involves synchronizing EDA data with driving behavior data, preprocessing the data, and conducting statistical analyses to identify patterns and correlations. The findings have significant implications for improving road safety, driver behavior, and potential interventions in the transportation industry.AI Generated
This summary of the content was generated with the help of AI.
AbstractThis study investigates the relationship between drivers’ electrodermal activity (EDA) and their eco-driving behaviours through real-time monitoring. Electrodermal activity, a physiological marker of sympathetic nervous system arousal, reflects emotional and cognitive states, providing a valuable window into drivers’ internal experiences. EDA and driving data were collected for 48 trips from 10 different drivers. Cluster analysis and the Pearson correlation coefficient was used to uncover potential patterns between driver EDA and their driving behaviour as measured using a driving score. The results follow the Yerkes-Dodson Law. Driving performance increase with EDA arousal, but only to a point. The investigation has implications for enhancing road safety, as it contributes to our understanding of how drivers’ emotional states influence their on-road performance. Additionally, it holds promise for developing innovative in-car systems that can adapt to drivers’ changing emotional states, promoting safer and more comfortable driving experiences. Ultimately, this study bridges the gap between psychophysiology and transportation, shedding light on the often-overlooked emotional aspects of driving behaviour.
-
- Title
- Advances in Asset Management: Strategies, Technologies, and Industry Applications
- Editors
-
Adolfo Crespo Márquez
Turuna S. Seecharan
Georges Abdul-Nour
Joe Amadi-Echendu
- Copyright Year
- 2024
- Publisher
- Springer Nature Switzerland
- Electronic ISBN
- 978-3-031-52391-5
- Print ISBN
- 978-3-031-52390-8
- DOI
- https://doi.org/10.1007/978-3-031-52391-5
PDF files of this book don't fully comply with PDF/UA standards, but do feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com