A data-driven distributed fault diagnosis scheme for large-scale systems based on correlation analysis
In: IET Control Theory & Applications, Jg. 18 (2024), Heft 2, S. 201 - 212
2024Artikel/Aufsatz in ZeitschriftOA Gold
ElektrotechnikFakultät für Ingenieurwissenschaften » Elektrotechnik und Informationstechnik » Automatisierungstechnik und komplexe Systeme
Titel in Englisch:
A data-driven distributed fault diagnosis scheme for large-scale systems based on correlation analysis
Autor*in:
Li, Zhennan
- LSF ID
- 12202
- ORCID
-
0000-0002-6387-6013
- Sonstiges
- der Hochschule zugeordnete*r Autor*in
korrespondierende*r Autor*in
- GND
- 134302427
- LSF ID
- 2347
- ORCID
-
0000-0002-5149-5918
- Sonstiges
- der Hochschule zugeordnete*r Autor*in
Erscheinungsjahr:
2024
Open Access?:
OA Gold
DuEPublico 2 ID
Scopus ID
Notiz:
OA Förderung 2023
Sprache des Textes:
Englisch
Schlagwort, Thema:
distributed algorithms ; fault diagnosis ; fault location
Ressourcentyp:
Text
Access Rights:
Open Access
Abstract in Englisch:
This paper studies data-driven distributed fault diagnosis for large-scale systems using sensor networks. To be specific, a distributed fault detection scheme based on correlation analysis is first proposed to improve the fault detection performance by minimizing the impact of noise-induced uncertainty. The core of the method is to implement the correlation of the coupled nodes to reduce the covariance of the residual signal in a distributed manner. Then, a fault localization approach is developed to locate the fault by measuring and comparing the degree of abnormality. A case study on Tennessee Eastman process is given in the end to demonstrate the proposed approach.