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Data-Driven Kalman Filtering in Nonlinear Systems with Actuator and Sensor Fault Diagnosis Based on Lyapunov Stability
| Content Provider | MDPI |
|---|---|
| Author | Fan, Lingling Guo, Kaipu Ji, Honghai Liu, Shida Wei, Yuzhou |
| Copyright Year | 2021 |
| Description | 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. |
| Starting Page | 2047 |
| e-ISSN | 20738994 |
| DOI | 10.3390/sym13112047 |
| Journal | Symmetry |
| Issue Number | 11 |
| Volume Number | 13 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-10-30 |
| Access Restriction | Open |
| Subject Keyword | Symmetry Industrial Engineering Information and Library Science Data-driven Filtering Dynamic Linearization Fault Diagnosis Recursive Least-squares |
| Content Type | Text |
| Resource Type | Article |
