Abstract: The Unsupervised Machine Learning aims to define a pattern in the set of data without previous knowledge of data features. Therefore, the first understanding of your data set can start by applying the Unsupervised Machine Learning methods to understand the how your dataset can be organized and if there´s a pattern of such dataset based on their independent variables. The concepts behind Unsupervised Machine Learning cluster a set of data without knowing previous classification or any information about the data. In order to cluster the data, the Unsupervised Machine Learning models the dataset and try to organize it in a cluster. The further step verifies the result based on error and finally, If the result is satisfactory, the new dataset can be used the model defined based on the previous dataset and then the model is validated.
Concerning the maintenance engineering, the type of data related to equipment encompasses physical characteristics as well as performance, cost of operations, cost of preventive maintenance, corrective maintenance, spare parts cost and RUL. Therefore, by defining some of such variables, it´s possible to group equipment with similar characteristics. In fact, the data organization based on its common features is the concept behind the clustering data that is the result of such unsupervised machine learning methods. This paper aims to demostrate the USML applied to maintenance planning optimization based on the RUL input from different equipment.