Publication details
Contribution to system failure occurrence prediction and to system remaining useful life estimation based on oil field data
| Authors | |
|---|---|
| Year of publication | 2015 |
| Type | Article in Periodical |
| Magazine / Source | Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability |
| MU Faculty or unit | |
| Citation | |
| Web | https://journals.sagepub.com/doi/abs/10.1177/1748006X14547789 |
| Doi | http://dx.doi.org/10.1177/1748006X14547789 |
| Field | Reliability and quality control, testing |
| Keywords | Failure prediction; system residual technical life estimation; field data; Wiener process |
| Description | At present, numerous approaches are devoted to monitoring a system state. Their intention is to determine the current state of a system and predict reliability parameters for the future. This article addresses one of the several possible approaches that allows us to determine a system technical state on the basis of diagnostic data. These diagnostic data are from the area of tribiodagnostics, namely, engine oil. The article examines iron and lead particles that are selected deliberately with respect to their origin in kinematic parts of the system and their degree of correlation with operation measures. The particles occur in oil during both operating time and calendar time development. To model their occurrence during operation time, we have used, in the first part of the article, a mathematical regression method to set parameters. In the second part, we have applied a diffusion model based on a Wiener process. The results confirm that we are able to estimate the residual technical life of a system. Moreover, the results enable us to schedule properly the intervals of preventive maintenance (oil change) and to plan a mission/operation. This results in optimising life cycle costs. It is assumed that the potential of the diagnostic data will be extracted by other approaches and methods. In the subsequent work, it will be useful to determine specific interval values of optimised preventive maintenance. |