Name
Abstract | ||
|---|---|---|
Lebesgue-sampling-based fault diagnosis and prognosis (LS-FDP) is developed with advantages of light computation and small uncertainty accumulation. In previous research, the applications of LS-FDP are developed for single-model degradation processes, which do not consider degradation processes that involve multiple modes with switching models. To address this scenario, this article proposes a modified interacting multiple model filter in the Lebesgue-time-space-model-based FDP framework. The design, analysis, and particle filtering-based implementation of the proposed method are discussed in detail. A simulation example and a real experiment are presented to demonstrate the accuracy, effectiveness, robustness, and universality of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TIE.2020.2970631 | IEEE Transactions on Industrial Electronics |
Keywords | DocType | Volume |
Fault diagnosis and prognosis,hidden Markov model,Lebesgue sampling,Lebesgue-time–space-model (LTSM),multimodel systems | Journal | 68 |
Issue | ISSN | Citations |
2 | 0278-0046 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dongzhen Lyu | 1 | 0 | 0.34 |
| Guangxing Niu | 2 | 0 | 0.34 |
| Bin Zhang | 3 | 231 | 21.88 |
| Gang Chen | 4 | 29 | 13.09 |
| Tao Yang | 5 | 160 | 76.32 |