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  • 1.
    Lindskog, Jakob
    et al.
    Halmstad University, School of Information Technology.
    Gunnarsson, Robin
    Halmstad University, School of Information Technology.
    Databearbetning på Ringhals2019Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    The new generation of digitalization has been ingrained into society. Algorithms and data models are controlling the news feed of social media, controlling the phone by interpreting voices and controlling the car, altogether with automonous vehicles. In the industries there is also an ongoing process where machine learning is applied to increase availability and reduce costs.

    The current paradigm for maintaining non-critical machines in the nuclear power industry is a combination of corrective maintenance and preventive maintenance. Corrective maintenance means doing repairs on the machine upon faults, preventive maintenance means doing repairs periodically. Both ways are costly because they run the risk of under- and over-maintaining the machine and therefore becoming resource-intensive. A paradigm shift is on it's way, and it's spelled Predictive Maintenance - being able to predict faults before they happen and plan maintenance thence.

    This report explores the possibilities of using LSTM and GRU to forecast potential damage on machines. This is based on data from measurements and historical issues on the machine.

    Download full text (pdf)
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