Analyzing Emerging Challenges for Data-Driven Predictive Aircraft Maintenance Using Agent-Based Modeling and Hazard Identification

Lee, Juseong; Mitici, Mihaela; Blom, H.A.P.; Bieber, Pierre; Freeman, Floris

doi:10.3390/aerospace10020186

Cited by 6 publications

(2 citation statements)

References 27 publications

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“…Moreover, the challenges identified, such as data quality issues, integration complexities, and cybersecurity concerns, underscore the importance of addressing these barriers to fully harness the potential of data-driven maintenance strategies. Collaborative efforts from policymakers, industry stakeholders, and researchers are needed to develop solutions and best practices for overcoming these challenges [35], [38], [39].…”

Section: Discussionmentioning

confidence: 99%

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

Judijanto,

Uhai,

Suri

2024

ESISCS

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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.

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Section: Discussionmentioning

confidence: 99%

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

Judijanto,

Uhai,

Suri

2024

ESISCS

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“…The study of Lee et al used ABM in analyzing emerging challenges for aircraft predictive maintenance. These studies represent ABM as a computational simulation approach where agents interact with one another and the environment with specified rules (Assaad et al, 2023;Lee et al, 2023).…”

Section: Methodsmentioning

confidence: 99%

Maintenance work management process model: incorporating system dynamics and 4IR technologies

Manenzhe

Telukdarie

Munsamy

2023

JQME

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PurposeThe purpose of this paper is to propose a system dynamic simulated process model for maintenance work management incorporating the Fourth Industrial Revolution (4IR) technologies.Design/methodology/approachThe extant literature in physical assets maintenance depicts that poor maintenance management is predominantly because of a lack of a clearly defined maintenance work management process model, resulting in poor management of maintenance work. This paper solves this complex phenomenon using a combination of conceptual process modeling and system dynamics simulation incorporating 4IR technologies. A process for maintenance work management and its control actions on scheduled maintenance tasks versus unscheduled maintenance tasks is modeled, replicating real-world scenarios with a digital lens (4IR technologies) for predictive maintenance strategy.FindingsA process for maintenance work management is thus modeled and simulated as a dynamic system. Post-model validation, this study reveals that the real-world maintenance work management process can be replicated using system dynamics modeling. The impact analysis of 4IR technologies on maintenance work management systems reveals that the implementation of 4IR technologies intensifies asset performance with an overall gain of 27.46%, yielding the best maintenance index. This study further reveals that the benefits of 4IR technologies positively impact equipment defect predictability before failure, thereby yielding a predictive maintenance strategy.Research limitations/implicationsThe study focused on maintenance work management system without the consideration of other subsystems such as cost of maintenance, production dynamics, and supply chain management.Practical implicationsThe maintenance real-world quantitative data is retrieved from two maintenance departments from company A, for a period of 24 months, representing years 2017 and 2018. The maintenance quantitative data retrieved represent six various types of equipment used at underground Mines. The maintenance management qualitative data (Organizational documents) in maintenance management are retrieved from company A and company B. Company A is a global mining industry, and company B is a global manufacturing industry. The reliability of the data used in the model validation have practical implications on how maintenance work management system behaves with the benefit of 4IR technologies' implementation.Social implicationsThis research study yields an overall benefit in asset management, thereby intensifying asset performance. The expected learnings are intended to benefit future research in the physical asset management field of study and most important to the industry practitioners in physical asset management.Originality/valueThis paper provides for a model in which maintenance work and its dynamics is systematically managed. Uncontrollable corrective maintenance work increases the complexity of the overall maintenance work management. The use of a system dynamic model and simulation incorporating 4IR technologies adds value on the maintenance work management effectiveness.

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The Emergent Perspective of Applied Observability in Occupational Health and Safety. The Exploratory Scoping Review for the Future Framework

Torrecilla-García,

Skotnicka,

Rubio-Romero

et al. 2023

Studies in Systems, Decision and Control

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Analyzing Emerging Challenges for Data-Driven Predictive Aircraft Maintenance Using Agent-Based Modeling and Hazard Identification

Cited by 6 publications

References 27 publications

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

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

Maintenance work management process model: incorporating system dynamics and 4IR technologies

The Emergent Perspective of Applied Observability in Occupational Health and Safety. The Exploratory Scoping Review for the Future Framework

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