Name
Title | ||
|---|---|---|
Remaining useful life prediction using prognostic methodology based on logical analysis of data and Kaplan---Meier estimation |
Abstract | ||
|---|---|---|
Most of the reported prognostic techniques use a small number of condition indicators and/or use a thresholding strategies in order to predict the remaining useful life (RUL). In this paper, we propose a reliability-based prognostic methodology that uses condition monitoring (CM) data which can deal with any number of condition indicators, without selecting the most significant ones, as many methods propose. Moreover, it does not depend on any thresholding strategies provided by the maintenance experts to separate normal and abnormal values of condition indicators. The proposed prognostic methodology uses both the age and CM data as inputs to estimate the RUL. The key idea behind this methodology is that, it uses Kaplan---Meier as a time-driven estimation technique, and logical analysis of data as an event-driven diagnostic technique to reflect the effect of the operating conditions on the age of the monitored equipment. The performance of the estimated RUL is measured in terms of the difference between the predicted and the actual RUL of the monitored equipment. A comparison between the proposed methodology and one of the common RUL prediction technique; Cox proportional hazard model, is given in this paper. A common dataset in the field of prognostics is employed to evaluate the proposed methodology. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s10845-014-0926-3 | Journal of Intelligent Manufacturing |
Keywords | Field | DocType |
RUL,Prognostics,Logical analysis of data,Kaplan–Meier estimation,CBM | Small number,Data mining,Proportional hazards model,Prognostics,Logical analysis of data,Condition monitoring,Artificial intelligence,Thresholding,Engineering,Machine learning | Journal |
Volume | Issue | ISSN |
27 | 5 | 0956-5515 |
Citations | PageRank | References |
13 | 0.63 | 14 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ahmed Ragab | 1 | 30 | 3.93 |
| Mohamed-Salah Ouali | 2 | 63 | 5.75 |
| Soumaya Yacout | 3 | 133 | 13.08 |
| Hany Osman | 4 | 19 | 1.41 |
| OualiMohamed-Salah | 5 | 13 | 0.63 |