Development of Predictive Maintenance Models for a Packaging Robot Based on Machine Learning
Résumé
This study presents the development of a predictive model for the health monitoring of power transmitters in a packaging robot usingmachine learning techniques. The model is based on a Discrete Bayesian Filter (DBF) and is compared to a model based on a Naïve Bayes Filter (NBF). Data preprocessing techniques are applied to select suitable descriptors for the predictive model. The results showthat theDBF model outperforms theNBFmodel in terms of predictive power. The model can be used to estimate the current state of the power transmitter and predict its degradation over time. This can lead to improved maintenance planning and cost savings in the context of Industry 4.0.
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