.png){kind=icon}
Return
Backstepping-based distributed abnormality localization for linear parabolic distributed parameter systems
.png){kind=icon}
PRE
AI
DOI：10.1016/j.automatica.2021.109930.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Abnormality localization for industrial processes described by distributed parameter systems (DPSs) is vital but seldom studied to the best of our knowledge. In this paper, a systematic framework is proposed to address this problem for a class of linear parabolic DPSs using a limited number of in-domain measurements plus one boundary measurement rather than the full-state measurement. The proposed methodology consists of an abnormality detection filter (ADF) and an abnormality localization filter (ALF) design based on the backstepping techniques and eigenspectrum. For the detection purpose, the residual is evaluated in a lumped manner; For the localization purpose, a distributed residual is first constructed and then evaluated in a distributed manner. Numerical simulations on a heat transfer rod are conducted to demonstrate the effectiveness of the proposed method. (C) 2021 Elsevier Ltd. All rights reserved.
Keywords:
Distributed parameter systems
Fault detection
Fault localization
Journal
IF:
5.9
Papers:
1.2W
Citations:
5.2W
Organization
Cited Papers
ShareSaveShareSave
ShareSaveEffect of nitrogen doping of graphene nanoflakes on their efficiency in supercapacitor applications
PREAI
ShareSaveShareSaveRobust actuator fault isolation and management in constrained uncertain parabolic PDE systems.png)
AUTOMATICA
AUTOMATICAIF5.9
PREAI
ShareSaveModel-based fault detection, estimation, and prediction for a class of linear distributed parameter systemsAUTOMATICA
OAAI
AUTOMATICAIF5.9
OAAIShareSaveShareSave
View more