New Trend In Enhancing Bearing Remaining Useful Life Prediction
| dc.contributor.author | Boukra, Tahar | |
| dc.contributor.author | Lebaroud, Abdessalam | |
| dc.date.accessioned | 2023-09-07T05:21:24Z | |
| dc.date.available | 2023-09-07T05:21:24Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | Generally the two main strategies taken in data-driven remaining useful life (RUL) prediction of a component/system can be summarized in 1) identifying a health indicator and predicting its trend until a predefined threshold; 2) mapping directly the health indicator (HI) to RUL by regression. Under the first category, traditional extracted features for RUL prediction usually show undesirable behaviors such as fluctuation, non-monotonicity and abrupt increase at the end which hampers the accuracy of the RUL prediction. To enhance the prediction accuracy, this paper brings a new feature selection method, based on preprocessing further the extracted features in a way that the identified prognostic feature results in an obvious trend quality. A set of established and proposed suitability metrics for the prognostic task are used to assess the identified features qualities. The Particle Filtering technique is adopted as a projection tool as well for the prediction of the RUL due to its capability to carry nonlinear systems in presence of non-Gaussian process/observation noise. Datasets from bearings run-to-failure experiments provided by FEMTO-ST Institute - IEEE PHM 2012 challenge- were used to validate our approach. A mean percentage error of 12.18% was achieved indicating that the model worked accurately and reliably on every tested bearing.. | ar |
| dc.identifier.issn | 2170-161X | |
| dc.identifier.issn | 2588-2082 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/15162 | |
| dc.language.iso | en | ar |
| dc.publisher | Oum-El-Bouaghi University | ar |
| dc.subject | Prognostics and Health Management | ar |
| dc.subject | Particle Filtering | ar |
| dc.subject | Remaining Useful Life | ar |
| dc.title | New Trend In Enhancing Bearing Remaining Useful Life Prediction | ar |
| dc.type | Article | ar |