Name
Title | ||
|---|---|---|
Data-Driven Real-Valued Timed-Failure-Propagation-Graph Refinement for Complex System Fault Diagnosis |
Abstract | ||
|---|---|---|
Timed Failure Propagation Graphs (TFPGs) have been widely used for the failure modeling and diagnosis of safety-critical systems. Currently most TFPGs are manually constructed by system experts, a process that can be time-consuming, error-prone, and even impossible for systems with highly nonlinear and machine-learning-based components. This letter proposes a new type of TFPGs, called Real-Valued Timed Failure Propagation Graphs (rTFPGs), designed for continuous-state systems. More importantly, it presents a systematic way of constructing rTFPGs by combining the powers of human experts and data-driven methods: first, an expert constructs a partial rTFPG based on his/her expertise; then a data-driven algorithm refines the rTFPG by adding nodes and edges based on a given set of labeled signals. The proposed approach has been successfully implemented and evaluated on three case studies. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/LCSYS.2020.3009932 | IEEE Control Systems Letters |
Keywords | DocType | Volume |
Failure diagnosis,signal temporal logic,spacecraft power system,timed failure propagation graphs | Journal | 5 |
Issue | ISSN | Citations |
3 | 2475-1456 | 1 |
PageRank | References | Authors |
0.37 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gang Chen | 1 | 1 | 0.37 |
| Xinfan Lin | 2 | 40 | 8.86 |
| Z. Kong | 3 | 303 | 19.21 |