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A Hybrid Machine Learning Approach for Predictive Maintenance in Smart Factories of the Future

Abstract : Advanced technologies based on Internet of Things (IOT) are blazing a trail to effective and efficient management of an overall plant. In this context, manufacturing companies require an innovative strategy to survive in a competitive business environment, utilizing those technologies. Guided by these requirements, the so-called predictive maintenance is of paramount importance and offers a significant potential for innovation to overcome the limitations of traditional maintenance policies. However, real shop-floors often have obstacles in providing insights to facilitate the effective management of assets in smart factories. Even if a significant amount of machine and process data is available, one of the common problems of these data is the lack of annotations describing the machine status or maintenance history. For this reason, companies have limited options to analyse manufacturing data, despite the capability of advanced machine learning techniques in supporting the identification of failure symptoms in order to optimize scheduling of maintenance operations. Moreover, each machine generates highly heterogeneous data, making it difficult to integrate all the information to provide data-driven decision support for predictive maintenance. Inspired by these challenges, this research provides a hybrid machine learning approach combining unsupervised learning and semi-supervised learning. The approach and result in this article are based on the development and implementation in a large collaborative EU-funded H2020 research project entitled BOOST 4.0 i.e. Big Data Value Spaces for COmpetitiveness of European COnnected Smart FacTories.
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Submitted on : Tuesday, July 9, 2019 - 1:46:11 PM
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Sangje Cho, Gökan May, Ioannis Tourkogiorgis, Roberto Perez, Oscar Lazaro, et al.. A Hybrid Machine Learning Approach for Predictive Maintenance in Smart Factories of the Future. IFIP International Conference on Advances in Production Management Systems (APMS), Aug 2018, Seoul, South Korea. pp.311-317, ⟨10.1007/978-3-319-99707-0_39⟩. ⟨hal-02177888⟩



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