Automated Diagnosis of Rolling Bearings using MRA and Neural Networks Articles uri icon

publication date

  • January 2010

start page

  • 289

end page

  • 299


  • 1


  • 24

International Standard Serial Number (ISSN)

  • 0888-3270

Electronic International Standard Serial Number (EISSN)

  • 1096-1216


  • Any industry needs an efficient predictive plan in order to optimize the management of resources and improve the economy of the plant by reducing unnecessary costs and increasing the level of safety. A great
    percentage of breakdowns in productive processes are caused by bearings.
    They begin to deteriorate from early stages of their functional life,
    also called the incipient level. This manuscript develops an automated
    diagnosis of rolling bearings based on the analysis and classification
    of signature vibrations. The novelty of this work is the application of
    the methodology proposed for data collected from a quasi-real industrial
    machine, where rolling bearings support the radial and axial loads the
    bearings are designed for. Multiresolution analysis (MRA) is used in a
    first stage in order to extract the most interesting features from
    signals. Features will be used in a second stage as inputs of a
    supervised neural network (NN) for classification purposes. Experimental
    results carried out in a real system show the soundness of the method
    which detects four bearing conditions (normal, inner race fault, outer
    race fault and ball fault) in a very incipient stage.