Economics at your fingertips  

Fault Propagation Inference Based on a Graph Neural Network for Steam Turbine Systems

Yi-Jing Zhang () and Li-Sheng Hu ()
Additional contact information
Yi-Jing Zhang: Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
Li-Sheng Hu: Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China

Energies, 2021, vol. 14, issue 2, 1-13

Abstract: A fault propagates along physical paths until it reaches the boundary of the equipment or system, which shows as a functional failure. Hence, inferring the fault propagation helps to ensure the normal operation of the industrial system. To infer the fault propagation in the steam turbine system, a graph model is developed. Firstly, a process graph topology is constructed according to the system mechanism, whose nodes and edges represent the equipment and mutual relationships. Meanwhile, a fault graph topology is built, in which nodes indicate potential faults and edges are inferred propagation paths. Then, the representations of fault nodes are realized through a graph neural network. Lastly, link prediction methods based on nodes’ representations are conducted, along with the paths inference results. Consequently, the accuracy of fault propagation inference for the steam turbine system is over 86%.

Keywords: fault propagation inference; steam turbine system; graph neural network; link prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link) (application/pdf) (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this article

Energies is currently edited by Prof. Dr. Enrico Sciubba

More articles in Energies from MDPI, Open Access Journal
Bibliographic data for series maintained by XML Conversion Team ().

Page updated 2021-09-11
Handle: RePEc:gam:jeners:v:14:y:2021:i:2:p:309-:d:476747