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Graph-Based Framework for Evaluating the Feasibility of Transition to Maintainomics

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Information Granularity, Big Data, and Computational Intelligence

Part of the book series: Studies in Big Data ((SBD,volume 8))

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Abstract

Maintenance is a powerful support function for ensuring equipment productivity, availability and safety. Nowadays, growing concern for timeliness, accuracy and the ability to offer tracking information led to the augmentation of e-technologies’ applications within maintenance management, i.e., e-maintenance. However, like any other information and communication (ICT)-based operation, massive data sets (i.e., big data) are generated from videos, audios, images, search queries, historic records, sensors, etc. Inevitably, e-maintenance needs to consider how to extract useful value from those raw and/or fused data as an important aspect before it can be adopted in any industry. This book chapter presents an overview of the e-maintenance data challenge. The main contribution of the article is the application of graph-theoretic approach (GTA) to the problem of finding an improved insight in the factors that determine the feasibility of maintainomics, i.e., data-centric maintenance. With such a concept, the maintenance-services can be upgraded from the low level of operations to the higher levels of planning and decision making.

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Authors and Affiliations

  1. Center for Asset Integrity Management (C-AIM), Department of Mechanical and Aeronautical Engineering, Faculty of Engineering, Built Environment and Information Technology, University of Pretoria, Pretoria, South Africa

    Bo Xing

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  1. Bo Xing
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Correspondence to Bo Xing .

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  1. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada

    Witold Pedrycz

  2. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Shyi-Ming Chen

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Xing, B. (2015). Graph-Based Framework for Evaluating the Feasibility of Transition to Maintainomics. In: Pedrycz, W., Chen, SM. (eds) Information Granularity, Big Data, and Computational Intelligence. Studies in Big Data, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-319-08254-7_5

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  • DOI: https://doi.org/10.1007/978-3-319-08254-7_5

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