Incremento del Tamaño de los Datos para la Detección de Fallos en Maquinaria Rotativa

In recent years, the use of data-based modeling techniques for the diagnosis of failures in rotating machinery has increased. These techniques require large amounts of data that cannot always be obtained because they generate high costs and excessive time, which are difficult to solve from the economic and technical point of view. The present work focuses on the preprocessing of vibration signals and proposes a method to increase the number of informative time series of a rotating machine without increasing time and costs in the signal acquisition stage. As a result, an expansion of 315 signals in the data acquisition phase to 429,000 after the application of the method has been obtained; Adequate amount for building data-driven models, including deep learning for fault detection in rotating machinery.