Development of a flexible predictive maintenance system in the context of Industry 4.0

Ciancio, Vincent; Homri, Lazhar; Dantan, Jean‐Yves; Siadat, Ali; Convain, Philippe

doi:10.1016/j.ifacol.2022.09.615

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…This operation requires a more flexible integrated system that ensures good proactive maintenance at the right time. In this context, Ciancio et al (2022) proposed a methodology for PM presented in the table 7. The PM approach is a very sensitive interconnected process, and is divided into two parts: "Better understand machines and processes" and "Better analyses objects and processes".…”

Section: Prognostics and Health Management (Phm)mentioning

confidence: 99%

Artificial intelligence-based predictive maintenance, time-sensitive networking, and big data-driven algorithmic decision-making in the economics of Industrial Internet of Things

Kliestik,

Nica,

Durana

et al. 2023

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Research background: The article explores the integration of Artificial Intelligence (AI) in predictive maintenance (PM) within Industrial Internet of Things (IIoT) context. It addresses the increasing importance of leveraging advanced technologies to enhance maintenance practices in industrial settings. Purpose of the article: The primary objective of the article is to investigate and demonstrate the application of AI-driven PM in the IIoT. The authors aim to shed light on the potential benefits and implications of incorporating AI into maintenance strategies within industrial environments. Methods: The article employs a research methodology focused on the practical implementation of AI algorithms for PM. It involves the analysis of data from sensors and other sources within the IIoT ecosystem to present predictive models. The methods used in the study contribute to understanding the feasibility and effectiveness of AI-driven PM solutions. Findings & value added: The article presents significant findings regarding the impact of AI-driven PM on industrial operations. It discusses how the implementation of AI technologies contributes to increased efficiency. The added value of the research lies in providing insights into the transformative potential of AI within the IIoT for optimizing maintenance practices and improving overall industrial performance.

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Section: Prognostics and Health Management (Phm)mentioning

confidence: 99%

Artificial intelligence-based predictive maintenance, time-sensitive networking, and big data-driven algorithmic decision-making in the economics of Industrial Internet of Things

Kliestik,

Nica,

Durana

et al. 2023

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“…M4.0 can be defined as an innovative and optimized maintenance strategy that integrates a traditional approach with I4.0 technologies. This combination of traditional maintenance management concepts with I4.0 technologies is transforming current maintenance practices [20].…”

Section: Maintenance Importance In Manufacturingmentioning

confidence: 99%

Application of Principle Component Analysis and logistic regression to support Six Sigma implementation in maintenance

Antosz,

Jasiulewicz - Kaczmarek,

Machado

et al. 2023

Eksploatacja i Niezawodność – Maintenance and Reliability

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Improving the efficiency of maintenance processes is one of the goals of companies. Improvement activities in this area require not only an appropriate maintenance strategy but also the use of a new approach to increase the efficiency of the process. This article focuses on using Six Sigma (SS) to improve maintenance processes. As an introduction, the generations of SS development are identified, and traditional and advanced analytical tools that can be useful in SS projects are reviewed. As part of the research, an example of the implementation of the SS project in the maintenance process using the DMAIC and selected advanced analytical methods, such as PCA and logistic regression, was presented. The PCA results showed that it was enough to have seven main components to keep about 84% of the information on variability. In developed logistic regression explained the impact of the individual factors affecting the availability of the machines. The identified factors and their interactions made it possible to define maintenance activities requiring improvements.

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Evaluation and Comparison of Selected Machine Learning Methods for Improving Maintenance Processes

Antosz,

Kulisz,

Husar

2024

IFAC-PapersOnLine

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Development of a flexible predictive maintenance system in the context of Industry 4.0

Cited by 5 publications

References 6 publications

Artificial intelligence-based predictive maintenance, time-sensitive networking, and big data-driven algorithmic decision-making in the economics of Industrial Internet of Things

Artificial intelligence-based predictive maintenance, time-sensitive networking, and big data-driven algorithmic decision-making in the economics of Industrial Internet of Things

Application of Principle Component Analysis and logistic regression to support Six Sigma implementation in maintenance

Evaluation and Comparison of Selected Machine Learning Methods for Improving Maintenance Processes

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