.png){kind=icon}
Return
State Estimation with Partial Random Walk
.png){kind=icon}
PRE
AI
DOI：10.1109/isie45063.2020.9152235.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, for a class of state estimation models with degradation process and mean reversion process, the optimal state estimation problem of measured data in partial random walk process is studied. In actual large mechanical systems, data collected by sensors will generate partial random walk characteristics according to system changes, which can be divided into two parts: long-range correlation characteristics and mean reversion process characteristics. In the traditional state estimation model, the influence of the correlation between the data on the state estimation is not considered, so the fitting degree of the actual state is not great. This paper presents an improved state estimation model based on measured data, through the Hurst index to identify the characteristics of the data, and according to the corresponding feature to modify estimation model. At then, by a group of NASA's lithium ion battery public data sets and a set of numerical simulation, respectively on two groups of properties are verified, prove the effectiveness of the proposed algorithm.
Keywords:
state estimation
long-range correlation process
mean reversion process
Journal
I
IF:
0
Papers:
50
Citations:
0
Organization
Cited Papers
Degradation Data Analysis Using a Wiener Degradation Model With Three-Source Uncertainties.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAIShareSaveShareSaveShareSaveShareSaveShareSaveShareSaveShareSave
View more