Name
Abstract | ||
|---|---|---|
This paper investigates the problem of fault detection and diagnosis in a class of nonlinear systems with modeling uncertainties. A nonlinear observer is first designed for monitoring fault. Radial basis function (RBF) neural network is used in this observer to approximate the unknown nonlinear dynamics. When a fault occurs, another RBF is triggered to capture the nonlinear characteristics of the fault function. The fault model obtained by the second neural network (NN) can be used for identifying the failure mode by comparing it with any known failure modes. Finally, a simulation example is presented to illustrate the effectiveness of the proposed scheme. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TNN.2009.2015078 | IEEE Transactions on Neural Networks |
Keywords | Field | DocType |
neural network,nonlinear characteristic,fault detection,nonlinear observer,failure mode,estimation method,fault model,nonlinear system,unknown nonlinear dynamic,fault function,known failure mode,control systems,radial basis function,nonlinear systems,nonlinear dynamics,signal processing,neural networks,hardware,uncertainty | Failure mode and effects analysis,Radial basis function,Nonlinear system,Computer science,Fault detection and isolation,Control theory,Artificial intelligence,Control system,Observer (quantum physics),Artificial neural network,Fault model,Machine learning | Journal |
Volume | Issue | ISSN |
20 | 5 | 1941-0093 |
Citations | PageRank | References |
18 | 0.84 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Su-Nan Huang | 1 | 505 | 61.65 |
| Kok Kiong Tan | 2 | 923 | 99.57 |