Abstract
Recent advancements in technologies such as in Big data and Internet of Things have made Predictive Maintenance (PdM) a key strategy to reduce unnecessary maintenance costs and improve product quality in the manufacturing sector. The premise of this paper is to implement and explore some of the most promising machine learning models for PdM, a Gradient Boosting (GB) model, and a Support Vector Machine (SVM) model. An innovative methodology for model training is proposed that aims to improve model performance while also allowing for continuous training. Furthermore, it is proposed an automatic hyperparameter tunning approach for the GB and SVM models. A synthetic dataset that reflects industrial machine data was used to validate the proposed methodology. The implemented models can achieve up to 0.92 recall and 94.55% accuracy, highlighting the effectiveness of the proposed methodology.
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Acknowledgments
The present work has received funding from European Regional Development Fund through COMPETE 2020 - Operational Programme for Competitiveness and Internationalisation through the P2020 Project MUWO (ANI|P2020 POCI-01-0247-FEDER-069417), and has been developed under the EUREKA - ITEA3 Project MUWO (ITEA-19022). We also acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/2020) to the project team.
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Mota, B., Faria, P., Ramos, C. (2023). Predictive Maintenance for Maintenance-Effective Manufacturing Using Machine Learning Approaches. In: García Bringas, P., et al. 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022). SOCO 2022. Lecture Notes in Networks and Systems, vol 531. Springer, Cham. https://doi.org/10.1007/978-3-031-18050-7_2
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