A Comparative Analysis of Machine Learning and Deep Learning Techniques for Component Fault Prediction
Ionut Cosmin Nedescu
A Comparative Analysis of Machine Learning and Deep Learning Techniques for Component Fault Prediction.
Rel. Andrea Bottino. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2023
Abstract
As the energy sector continues to grow and evolve, the need for advanced maintenance techniques that ensure the reliability and safety of electrical components becomes increasingly important. The adoption of predictive maintenance has become a major concern for companies that require regular component replacement. Enterprises are investing in creating predictive models that anticipate component failure to meet the demands of today's decade. Predictive maintenance is a very useful and powerful tool to ensure component reliability, as it allows companies to monitor the health of their assets and detect potential failures before they occur. Given the large amount of data generated by modern electrical systems, it is very challenging to select an appropriate classification model for predictive maintenance.
In this thesis work, we compare two different classification models used for predictive maintenance of electrical components of an Italian energy company: eXtreme Gradient Boosting (XGBoost) and Restricted Boltzmann Machine used as a stand-alone classifier (discrete RBM)
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