A Big Data Analytical Architecture for the Asset Management

Jaime Campos, Pankaj Sharma, Unai Gorostegui Gabiria, Erkki Jantunen, David Baglee

    Research output: Contribution to journalArticleScientificpeer-review

    24 Citations (Scopus)

    Abstract

    The paper highlights the characteristics of data and big data analytics in manufacturing, more specifically for the industrial asset management. The authors highlight important aspects of the analytical system architecture for purposes of asset management. The authors cover the data and big data technology aspects of the domain of interest. This is followed by application of the big data analytics and technologies, such as machine learning and data mining for asset management. The paper also presents the aspects of visualisation of the results of data analytics. In conclusion, the architecture provides a holistic view of the aspects and requirements of a big data technology application system for purposes of asset management. The issues addressed in the paper, namely equipment health, reliability, effects of unplanned breakdown, etc., are extremely important for today's manufacturing companies. Moreover, the customer's opinion and preferences of the product/services are crucial as it gives an insight into the ways to improve in order to stay competitive in the market. Finally, a successful asset management function plays an important role in the manufacturing industry, which is dependent on the support of proper ICTs for its further success.
    Original languageEnglish
    Pages (from-to)369-374
    Number of pages6
    JournalProcedia CIRP
    Volume64
    DOIs
    Publication statusPublished - 2017
    MoE publication typeA1 Journal article-refereed

    Keywords

    • asset management
    • big data
    • big data analytics
    • data mining

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  • Cite this

    Campos, J., Sharma, P., Gabiria, U. G., Jantunen, E., & Baglee, D. (2017). A Big Data Analytical Architecture for the Asset Management. Procedia CIRP, 64, 369-374. https://doi.org/10.1016/j.procir.2017.03.019