Deterministic particle filtering and application to diagnosis of a roller bearing

  • Authors:

  • Ouafae Bennis;Frédéric Kratz

  • Affiliations:

  • Institut PRISME, Université d'Orléans, Chartres, France;Institut PRISME – ENSI de Bourges, Bourges, France

  • Venue:
  • NOLISP'09 Proceedings of the 2009 international conference on Advances in Nonlinear Speech Processing
  • Year:
  • 2009

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Abstract

In this article, the detection of a fault on the inner race of a roller bearing is presented as a problem of optimal estimation of a hidden fault, via measures delivered by a vibration sensor. First, we propose a linear model for the transmission of a vibratory signal to the sensor's diaphragm. The impact of shocks due to the default is represented by a stochastic drift term whose values are in a discrete set. To determine the state of the roller bearing, we estimate the value of this term using particular filtering.