Data Size Increment for Fault Detection on Rotating Machinery

Research output: Contribution to journalArticle


In recent years, the use of data-driven 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 pre-processing of vibration signals and proposes a method to increase the number of informative time series of a rotating machine without increasing the time and costs in the signal acquisition stage. As a result, an increase from 315 signals in the data acquisition phase to 429000 after the application of the method has been obtained; an adequate amount for the construction of data-based models, including deep learning for the detection of faults in rotating machinery.
Translated title of the contributionIncremento del tamaño de los datos para la detección de fallas en maquinaria rotativa
Original languageEnglish (US)
Pages (from-to)41-48
Number of pages8
Issue number17
StatePublished - 20 Jan 2019


  • Bearings
  • Data acquisition
  • Pre-processing
  • Signals

CACES Knowledge Areas

  • 727A Industrial and process design


Dive into the research topics of 'Data Size Increment for Fault Detection on Rotating Machinery'. Together they form a unique fingerprint.

Cite this