Journal of Robotics, Networking and Artificial Life

Volume 5, Issue 3, December 2018, Pages 149 - 152

Sticking Fault Detecting Method for CARIMA Model

Authors
Toyoaki Tanikawa1, Henmi Tomohiro2, *
1Advanced Course in Industrial and Systems Engineering, National Institute of Technology, Kagawa College, Takamatsu, Kagawa 761-8058, Japan
2Department of Electrical and Computer Engineering, National Institute of Technology, Kagawa College, Takamatsu, Kagawa 761-8058, Japan
Corresponding Author
Henmi Tomohiro
Received 10 August 2018, Accepted 19 October 2018, Available Online 1 December 2018.
DOI
10.2991/jrnal.2018.5.3.1How to use a DOI?
Keywords
Fault detection; sticking fault; CARIMA model
Abstract

This paper proposes a sticking fault detecting method for controlled auto-regressive integrated moving average model (CARIMA) which detect the sticking fault of control input and feedback signal. It consists of model estimation using recursive least square method with the forgetting factor and fault detection. In the fault detection, an evaluation function is introduced, and it generates a fault signal from the input and output data. Numerical simulations are performed, and it is shown that this method can detect the sticking fault.

Copyright
© 2018 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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Journal
Journal of Robotics, Networking and Artificial Life
Volume-Issue
5 - 3
Pages
149 - 152
Publication Date
2018/12/01
ISSN (Online)
2352-6386
ISSN (Print)
2405-9021
DOI
10.2991/jrnal.2018.5.3.1How to use a DOI?
Copyright
© 2018 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Toyoaki Tanikawa
AU  - Henmi Tomohiro
PY  - 2018
DA  - 2018/12/01
TI  - Sticking Fault Detecting Method for CARIMA Model
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 149
EP  - 152
VL  - 5
IS  - 3
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.2018.5.3.1
DO  - 10.2991/jrnal.2018.5.3.1
ID  - Tanikawa2018
ER  -