.png){kind=icon}
Return
Diagnostic tools for multivariable model-based control systems
.png){kind=icon}
PRE
AI
DOI：10.1021/ie9606653.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper we propose a set of diagnostic tools that can be used for detecting and estimating the causes for performance degradation in multivariable model-based control systems. Two simple diagnostic tests are proposed, on the basis of prediction error analysis, to detect the performance degradation and bifurcate the potential causes for the degradation. These tests are useful for narrowing down the list of potential causes for performance degradation. For detailed fault estimation, we propose to use a stochastic model based on Gaussian-sum statistics. The optimal estimation for this model requires a bank of a growing number of filters and is computationally intractable. To propose a practical solution, we combine the different suboptimal approaches in the literature. The results of the diagnosis can be used for alerting the operators about the potential faults and for adapting the controllers.
Keywords:
STATE ESTIMATION
FAULT-DIAGNOSIS
PERFORMANCE
PLANTS
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
I
IF:
3.9
Papers:
4.0W
Citations:
9.6W
Organization
No organization information available
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSaveShareSaveElliptic Eigenvalue Problems with Large Drift and Applications to Nonlinear Propagation Phenomena
OAAI
OAAIShareSaveShareSave
View more