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Maintenance Optimization Through predictive Maintenance: A Case Study For Damen Shipyards

Dekker, K.J. (2020) Maintenance Optimization Through predictive Maintenance: A Case Study For Damen Shipyards.

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Abstract:We use predictive modelling techniques to predict the remaining useful life of critical components on ships. Specifically, we use a recurrent neural network and a gradient boosting tree to predict the remaining useful life of the fuel separator. We found the gradient boosting tree to be a suitable model for this prediction task. When optimizing a maintenance threshold, the boosting tree outperforms a baseline time-based preventive maintenance model (a 1.4% cost reduction).
Item Type:Essay (Master)
Clients:
Damen Shipyards, Gorinchem, Netherlands
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:01 general works, 50 technical science in general, 58 process technology, 85 business administration, organizational science
Programme:Industrial Engineering and Management MSc (60029)
Link to this item:http://purl.utwente.nl/essays/80852
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