Informace o publikaci
Perspective analysis outcomes of selected tribodiagnostic data used as input for condition based maintenance
| Autoři | |
|---|---|
| Rok publikování | 2016 |
| Druh | Článek v odborném periodiku |
| Časopis / Zdroj | Reliability engineering & system safety |
| Fakulta / Pracoviště MU | |
| Citace | |
| Doi | http://dx.doi.org/10.1016/j.ress.2015.07.026 |
| Obor | Obecná matematika |
| Klíčová slova | Field data assessment; Off-line diagnostics; First hitting time; System residual technical life; Maintenance optimization; Diffusion processes |
| Popis | The aim of this article is to estimate system soft failure occurrence and residual technical life in order to optimise firmly planned preventive maintenance. To do this, selected wear particles from oil field data are analysed. Using a large tribodiagnostic dataset we estimate the residual technical life of the observed system statistically. The dataset includes information about particles contained in oil which testify to oil conditions as well as system conditions. We focus here on the wear particles which we (and other analysts) consider to be interesting, ferrum (Fe) and lead (Pb), regarded as contact degradation and wear products. By modelling the occurrence of particles in oil we plan to determine the expected moment when soft failure occurs; this moment might then be set as the time to perform preventive maintenance (PM). Both operation time and calendar time are used here for modelling, for soft failure occurrence determination and for residual technical life estimation. The modelling is based on the specific characteristics of two diffusion processes, the Wiener process (WP) with positive drift and the OrnsteinUhlenbeck process (OUP). We also applied a fuzzy inference system to support our first results from the diffusion processes as there is a level of uncertainty and fuzziness in the oil field data. Following the modelling outcomes we are able to judge the system hazard rate, predict expected mean residual life and set up principles of "condition based maintenance" (CBM) for this system. However, the possible uses of our outcomes are much wider. For example, they can be used as inputs for operation and mission planning, and life cycle costs can be significantly reduced thanks to the maintenance optimisation. |