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The purpose of this thesis is to evaluate unsupervised clustering strategies for data associated with bulk carrier trucks, in the detection of failure patterns in thermal systems.The study of these techniques is important in the field of data-based predictive maintenance with the implementation of machine learning algorithms that allow proper planning of maintenance schedules in freight transport companies.For the development of the thesis, the telemetry devices of the bulk tractor trucks of a Colombian cargo transport company are used as a source of information, which report data in real time of the measurement of variables such as speed, temperatures, state of operation of the vehicle, among others for the year 2020.The history of workshop entries of the fleet of 116 tractor-trailers is also used, where workshop entries for the intervention of thermal systems are analyzed.These data are the input for the evaluation of the grouping strategies proposed in this work.The results start from obtaining the data, preparing them and descriptive analysis to implement dimensionality reduction techniques in the information and subsequently evaluate the behavior of grouping algorithms for the detection of failure patterns that are related to damage in thermal systems.With the development of this work, there is a potential for savings in corrective costs of the fleet in the workshop that points to an adequate management of the fleet in pay-per-use models, leveraging the availability of vehicles in transport operations.