.png){kind=icon}
Return
Prognostics 101: A tutorial for particle filter-based prognostics algorithm using Matlab
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ress.2013.02.019.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper presents a Matlab-based tutorial for model-based prognostics, which combines a physical model with observed data to identify model parameters, from which the remaining useful life (RUL) can be predicted. Among many model-based prognostics algorithms, the particle filter is used in this tutorial for parameter estimation of damage or a degradation model. The tutorial is presented using a Matlab script with 62 lines, including detailed explanations. As examples, a battery degradation model and a crack growth model are used to explain the updating process of model parameters, damage progression, and RUL prediction. In order to illustrate the results, the RUL at an arbitrary cycle are predicted in the form of distribution along with the median and 90% prediction interval. This tutorial will be helpful for the beginners in prognostics to understand and use the prognostics method, and we hope it provides a standard of particle filter based prognostics. (C) 2013 Elsevier Ltd. All rights reserved.
Keywords:
Battery degradation
Crack growth
Matlab code
Model-based prognostics
Particle filter
Remaining useful life
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
R
IF:
11
Papers:
9.0K
Citations:
4.2W
Organization
Cited Papers
ShareSave
ShareSaveStructural brain alterations in patients with major depressive disorder and high risk for suicide: Evidence for a distinct neurobiological entity?.png)
NeuroImage
NeuroImageIF0
PREAI
ShareSaveShareSaveShareSave
ShareSave
View more