| タイトル | Uncertainty Management for Diagnostics and Prognostics of Batteries using Bayesian Techniques |
| 著者（英） | Saha, Bhaskar; Goebel, kai |
| 著者所属（英） | NASA Ames Research Center |
| 発行日 | 2007-12-21 |
| 言語 | eng |
| 内容記述 | Uncertainty management has always been the key hurdle faced by diagnostics and prognostics algorithms. A Bayesian treatment of this problem provides an elegant and theoretically sound approach to the modern Condition- Based Maintenance (CBM)/Prognostic Health Management (PHM) paradigm. The application of the Bayesian techniques to regression and classification in the form of Relevance Vector Machine (RVM), and to state estimation as in Particle Filters (PF), provides a powerful tool to integrate the diagnosis and prognosis of battery health. The RVM, which is a Bayesian treatment of the Support Vector Machine (SVM), is used for model identification, while the PF framework uses the learnt model, statistical estimates of noise and anticipated operational conditions to provide estimates of remaining useful life (RUL) in the form of a probability density function (PDF). This type of prognostics generates a significant value addition to the management of any operation involving electrical systems. |
| NASA分類 | Electronics and Electrical Engineering |
| レポートNO | IEEEAC Paper 1361 |
| 権利 | Copyright |
| URI | https://repository.exst.jaxa.jp/dspace/handle/a-is/261432 |
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