Application of Machine Learning Methods on Predictive Maintenance

Maintenance is important in optimization of the business value of a functional unit and this optimization can only be achieved through predictive maintenance which ensures that maintenance activities are not carried out before due time and at the same time, prevent occurrence of breakdown of functional units because of missed maintenance activities.In this thesis, we focus on using different machine learning methods to predict the failure of a functional units.We explore the data and use missing data techniques to deal with missing values in the dataset, which resulted in a complete dataset.We explore various feature selection techniques to extract important features and reduce dimensionality of the dataset.Then, we explore the following machine learning methods: logistic regression, naïve Bayes, support vector machine, k-nearest neighbour and ensemble learning techniques which are bagging and boosting methods.Our results indicated that predictions from ensemble learning techniques have better evaluation metrics compared to other machine learning methods.