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Fuzzified Case-Based Reasoning Blockchain Framework for Predictive Maintenance in Industry 4.0

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Data Analytics and Computational Intelligence: Novel Models, Algorithms and Applications

Part of the book series: Studies in Big Data ((SBD,volume 132))

Abstract

Production losses can influence minor and big businesses and frequently result from unpredicted glitches that arise during what should be a repetitive maintenance procedure. However, procedures can be taken to curtail losses through appropriate prediction and planning. Smart businesses or Industry 4.0 are data-driven and largely count on computational intelligence for the generation, collection, transportation, sharing, storing, and transforming of data into value. In light of the incessant development of computational intelligence, several measures to reduce production losses as related to maintenance cost reduction have also been detected progressively but not totally covered because these measures have been rapidly adopted without prior consideration, posing a challenge in production because the means of predicting the effect of such adoption may not be foreseen. Hence, leading to production loss. This has led to the motive for developing a fuzzified case-based reasoning blockchain framework for predictive maintenance in Industry 4.0. The fuzzified case-based reasoning blockchain (FCBRB) framework is a convincing model that permits the use of fuzzy-based rules to ease the uncertainty in case similarity knowledge and the establishment of a blockchain for case knowledge authentication and authorization. The method implemented in this study proposed the use of fuzzy logic, blockchain, and case-based reasoning (CBR) for up-to-the-minute reasoning tasks in the classification of suspected cases of production loss before and after routine maintenance. The method has been implemented in a prototype web application written in HTML and JavaScript with a K-NN machine-learning algorithm. The findings indicated that the maintenance case knowledge can be shared securely and efficiently for predictive maintenance in Industry 4.0.

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Authors and Affiliations

  1. McPherson University, Seriki Sotayo, Ogun State, Nigeria

    Kayode Abiodun Oladapo

  2. Lead City University, Ibadan, Oyo State, Nigeria

    Folasade Adedeji

  3. Babcock University, Ikenne, Ogun State, Nigeria

    Uchenna Jeremiah Nzenwata

  4. University of Silesia, Sosnowiec, Poland

    Bao Pham Quoc

  5. Directorate of Baptist Mission Schools, Ibadan, Oyo State, Nigeria

    Akinbiyi Dada

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  1. Kayode Abiodun Oladapo
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  2. Folasade Adedeji
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  3. Uchenna Jeremiah Nzenwata
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  4. Bao Pham Quoc
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  5. Akinbiyi Dada
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Correspondence to Kayode Abiodun Oladapo .

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Editors and Affiliations

  1. División Multidisciplinaria de Ciudad Universitaria, Universidad Autónoma de Ciudad Juárez, Chihuahua, Mexico

    Gilberto Rivera

  2. Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Madero, Ciudad Madero, Tamaulipas, Mexico

    Laura Cruz-Reyes

  3. School of Engineering, Universidad de Cádiz, Cadiz, Spain

    Bernabé Dorronsoro

  4. Universidad Tecnológica de La Habana “José Antonio Echeverría”, La Habana, Cuba

    Alejandro Rosete

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Oladapo, K.A., Adedeji, F., Nzenwata, U.J., Quoc, B.P., Dada, A. (2023). Fuzzified Case-Based Reasoning Blockchain Framework for Predictive Maintenance in Industry 4.0. In: Rivera, G., Cruz-Reyes, L., Dorronsoro, B., Rosete, A. (eds) Data Analytics and Computational Intelligence: Novel Models, Algorithms and Applications. Studies in Big Data, vol 132. Springer, Cham. https://doi.org/10.1007/978-3-031-38325-0_12

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