Characteristics of Maintenance 4.0 and Their Reflection in Aircraft Engine MRO

Metso, Lasse; Thenent, Nils E.

doi:10.1007/978-3-030-57745-2_42

Cited by 6 publications

(5 citation statements)

References 35 publications

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“…Condition monitoring to prevent baggage cart failure in real time was applied by the authors, and was additionally enriched by autonomous systems linked to artificial intelligence and Industry 4.0 airport logic. In Metso and Thenent [ 387 ], the authors also investigated aspects of condition monitoring in connection to maintenance 4.0 for air transport; therefore, it may be suspected that this would be a new trend in the condition monitoring of rail transport facilities.…”

Section: Systematic Literature Reviewmentioning

confidence: 99%

Condition Monitoring of Rail Transport Systems: A Bibliometric Performance Analysis and Systematic Literature Review

Kostrzewski

Melnik

2021

Sensors

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Condition monitoring of rail transport systems has become a phenomenon of global interest over the past half a century. The approaches to condition monitoring of various rail transport systems—especially in the context of rail vehicle subsystem and track subsystem monitoring—have been evolving, and have become equally significant and challenging. The evolution of the approaches applied to rail systems’ condition monitoring has followed manual maintenance, through methods connected to the application of sensors, up to the currently discussed methods and techniques focused on the mutual use of automation, data processing, and exchange. The aim of this paper is to provide an essential overview of the academic research on the condition monitoring of rail transport systems. This paper reviews existing literature in order to present an up-to-date, content-based analysis based on a coupled methodology consisting of bibliometric performance analysis and systematic literature review. This combination of literature review approaches allows the authors to focus on the identification of the most influential contributors to the advances in research in the analyzed area of interest, and the most influential and prominent researchers, journals, and papers. These findings have led the authors to specify research trends related to the analyzed area, and additionally identify future research agendas in the investigation from engineering perspectives.

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Section: Systematic Literature Reviewmentioning

confidence: 99%

Condition Monitoring of Rail Transport Systems: A Bibliometric Performance Analysis and Systematic Literature Review

Kostrzewski

Melnik

2021

Sensors

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“…Additionally, the deployment of Artificial Intelligence, encompassing supervised Artificial Neural Networks and Convolutional Neural Networks, has proven effective in real-time condition monitoring and fault detection with high diagnostic accuracy [110]. These methods have demonstrated their capability to automatically identify, diagnose, and rectify various categories of sensor faults, including positive bias, negative bias, precision degradation, and drift [111]. Researchers and practitioners can refer to these studies to discern appropriate methods for automated fault detection and diagnostics in building systems [112].…”

Section: Ai For Fault Detection and Diagnosticsmentioning

confidence: 99%

Advancing Sustainable Energy Management: A Comprehensive Review of Artificial Intelligence Techniques in Building

Hanafi,

Moawed,

Abdellatif

2024

Engineering Research Journal (Shoubra)

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This paper explores artificial intelligence's (AI) transformative potential in optimizing energy management within buildings, aligning with environmental objectives and sustainable practices. AI-based methodologies are pivotal in identifying inefficiencies, forecasting future energy requirements, and mitigating energy wastage. Adopting AI-driven energy management systems enhances efficiency, reduces costs, and contributes to a decreased building environmental footprint. Furthermore, AI empowers buildings to actively participate in energy markets by accurately predicting real-time supply and demand without operational disruption. The study delves into various AI applications, including energy prediction, optimization, fault detection and diagnosis (FDD), and real-world implementations. Notably, AI's role in fault detection and diagnostics is highlighted, emphasizing its substantial contribution to diagnostic precision. Specific numerical outcomes from reviewed studies underscore the tangible impact of AI techniques. Predictive control powered by AI achieved a remarkable 20% reduction in heating energy without compromising comfort. Additionally, smart home energy management algorithms demonstrated a notable 22.63% decrease in electricity costs and a 22.77% reduction in the peak-to-average ratio. These concrete figures underscore the practical success of AI techniques in significantly reducing energy consumption. This review affirms the transformative potential of AI in building energy management. Including specific numerical values from empirical studies adds a quantitative dimension to the discussion, providing clear evidence of the positive impact of AI on energy efficiency.

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“…In the light of the technological evolution of production systems and increased integration of hard-and software including analytical capabilities, often associated with the term "Industry 4.0", the approaches to maintenance evolve, too. In particular condition-monitoring and predictive capabilities enable a shift away from general predefined plans to more targeted condition-specific maintenance actions (Metso & Thenent, 2020).…”

Section: How Does Agile Fit Into Industrial Maintenance?mentioning

confidence: 99%

Agile Methods in Industrial Maintenance

Metso

Thenent

2023

Lecture Notes in Mechanical Engineering

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Agile has become a popular term in organisations and businesses, beyond the IT sector. Characteristics of agile ways of working such as the assumption that boundary conditions and requirements might (and will) change make them attractive to applications in dynamic environments in general. Methods such as Scrum or Xtreme Programming turn agile values into actionable descriptions, originally intended for software development. This work offers a literature-based assessment of the suitability of agile methods for the maintenance of industrial production systems. The findings show that so far there has been a limited attention to application of agile methods in in the industrial maintenance context but at the same time suggest the potential for their application.

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Characteristics of Maintenance 4.0 and Their Reflection in Aircraft Engine MRO

Cited by 6 publications

References 35 publications

Condition Monitoring of Rail Transport Systems: A Bibliometric Performance Analysis and Systematic Literature Review

Condition Monitoring of Rail Transport Systems: A Bibliometric Performance Analysis and Systematic Literature Review

Advancing Sustainable Energy Management: A Comprehensive Review of Artificial Intelligence Techniques in Building

Agile Methods in Industrial Maintenance

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