Adaptive Modeling of Reliability Properties for Control and Supervision Purposes

  • Authors:

  • Kai-Uwe Dettmann;Dirk SéOuffker

  • Affiliations:

  • Chair of Dynamics and Control, University of Duisburg-Essen, LotharstraésZe 1, 47057 Duisburg, Germany;Chair of Dynamics and Control, University of Duisburg-Essen, LotharstraésZe 1, 47057 Duisburg, Germany

  • Venue:
  • International Journal of Applied Mathematics and Computer Science - Issues in Advanced Control and Diagnosis
  • Year:
  • 2011

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Abstract

Modeling of reliability characteristics typically assumes that components and systems fail if a certain individual damage level is exceeded. Every (mechanical) system damage increases irreversibly due to employed loading and (mechanical) stress, respectively. The main issue of damage estimation is adequate determination of the actual state-of-damage. Several mathematical modeling approaches are known in the literature, focusing on the task of how loading effects damage progression (e.g., WéoUhler, 1870) for wear processes. Those models are only valid for specific loading conditions, a priori assumptions, set points, etc. This contribution proposes a general model, covering adequately the deterioration of a set of comparable systems under comparable loading. The main goal of this contribution is to derive the loading-damage connection directly from observation without assuming any damage models at the outset. Moreover, the connection is not investigated in detail (e.g., to examine the changes in material, etc.) but only approximated with a probabilistic approach. The idea is subdivided into two phases: A problem-specific relation between loading applied (to a structure, which contributes to the stress) and failure is derived from simulation, where a probabilistic approach only assumes a distribution function. Subsequently, an adequate general model is set up to describe deterioration progression. The scheme will be shown through simulation-based results and can be used for estimation of the remaining useful life and predictive maintenance/control.