Name
Title | ||
|---|---|---|
Adaptive Approximation for Multiple Sensor Fault Detection and Isolation of Nonlinear Uncertain Systems |
Abstract | ||
|---|---|---|
This paper presents an adaptive approximation-based design methodology and analytical results for distributed detection and isolation of multiple sensor faults in a class of nonlinear uncertain systems. During the initial stage of the nonlinear system operation, adaptive approximation is used for online learning of the modeling uncertainty. Then, local sensor fault detection and isolation (SFDI) modules are designed using a dedicated nonlinear observer scheme. The multiple sensor fault isolation process is enhanced by deriving a combinatorial decision logic that integrates information from local SFDI modules. The performance of the proposed diagnostic scheme is analyzed in terms of conditions for ensuring fault detectability and isolability. A simulation example of a single-link robotic arm is used to illustrate the application of the adaptive approximation-based SFDI methodology and its effectiveness in detecting and isolating multiple sensor faults. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TNNLS.2013.2250301 | Neural Networks and Learning Systems, IEEE Transactions |
Keywords | Field | DocType |
combinatorial mathematics,fault diagnosis,nonlinear systems,observers,uncertain systems,SFDI modules,adaptive approximation based SFDI methodology,adaptive approximation based design methodology,combinatorial decision logic,diagnostic scheme,distributed detection,fault detectability,isolability,isolating multiple sensor faults,isolation modules,local sensor fault detection,modeling uncertainty,multiple sensor fault detection,multiple sensor fault isolation process,nonlinear observer scheme,nonlinear system operation,nonlinear uncertain systems,online learning,single link robotic arm,Adaptive estimation,fault detection,fault diagnosis,learning systems | Online learning,Robotic arm,Decision table,Nonlinear system,Fault detection and isolation,Computer science,Control theory,Nonlinear observer,Design methods,Uncertain systems | Journal |
Volume | Issue | ISSN |
25 | 1 | 2162-237X |
Citations | PageRank | References |
21 | 0.84 | 21 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vasso Reppa | 1 | 51 | 7.18 |
| Marios Polycarpou | 2 | 2020 | 206.96 |
| Panayiotou, C.G. | 3 | 201 | 18.81 |