Name
Title | ||
|---|---|---|
Data-Driven Kalman Filtering in Nonlinear Systems with Actuator and Sensor Fault Diagnosis Based on Lyapunov Stability |
Abstract | ||
|---|---|---|
This study proposes a data-driven adaptive filtering method for the fault diagnosis (DDAF-FD) of discrete-time nonlinear systems and provides a simultaneous online estimation of actuator and sensor faults. First, dynamic linearization was adopted to transform the nonlinear system into a quasi-linear model, which facilitated accurate modeling of the nonlinear system. Second, a data-driven adaptive fault diagnosis method was designed under the framework of data-driven filtering and the recursive least-squares algorithm using system I/O data only, and accurate real-time estimation of two fault factors was achieved. In addition, the simulation results demonstrate the effectiveness of the proposed method. The stability was verified via the Lyapunov method. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/sym13112047 | SYMMETRY-BASEL |
Keywords | DocType | Volume |
data-driven filtering, dynamic linearization, fault diagnosis, recursive least-squares | Journal | 13 |
Issue | Citations | PageRank |
11 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lingling Fan | 1 | 0 | 0.34 |
| Kaipu Guo | 2 | 0 | 0.34 |
| Honghai Ji | 3 | 0 | 1.01 |
| Shida Liu | 4 | 0 | 0.68 |
| Yuzhou Wei | 5 | 0 | 0.68 |