Detección de fallas mecánicas mediante "Machine Learning", utilizando el clasificador "Random Forest"

Abstract

XV CIBIM -2022, Madrid "Mechanical Backlash", respectively. This is due to the low number of signals available for testing, only 28, and because the excitations are related to fr and its harmonics, which causes "Confusion". In a similar way to the previous reasoning, "Mechanical clearance + Misalignment" presented the percentage of correctness of 46.9%, with an error contribution of 40.4% due to "Unbalance". This is also due to the same reasons as before, with the number of signals available for tests being even smaller, that is, only 18. To increase assertiveness, it is necessary to have a greater number of signals, or to apply the "Data Augmentation" method to increase the number of signals or even extract more discriminative parameters in the model. The results show that the proposed methodology allowed the detection of supervised failures in rotating machines, being a promising tool to be applied in Industry 4.0.