Name
Abstract | ||
|---|---|---|
In this article, a parallel radial basis function network in conjunction with chaos theory (CPRBF network) is presented, and applied to practical fault detection for hydraulic pump, which is a critical component in aircraft. The CPRBF network consists of a number of radial basis function (RBF) subnets connected in parallel. The number of input nodes for each RBF subnet is determined by different embedding dimension based on chaotic phase-space reconstruction. The output of CPRBF is a weighted sum of all RBF subnets. It was first trained using the dataset from normal state without fault, and then a residual error generator was designed to detect failures based on the trained CPRBF network. Then, failure detection can be achieved by the analysis of the residual error. Finally, two case studies are introduced to compare the proposed CPRBF network with traditional RBF networks, in terms of prediction and detection accuracy. © 2011 Lu et al; licensee Springer. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1186/1687-6180-2011-49 | EURASIP J. Adv. Sig. Proc. |
Keywords | Field | DocType |
chaotic parallel radial basis function (cprbf),fault detection,hydraulic pump,residual error generator,time series prediction | Residual,Radial basis function network,Radial basis function,Embedding,Fault detection and isolation,Computer science,Subnet,Artificial intelligence,Hydraulic pump,Chaotic,Machine learning | Journal |
Volume | Issue | ISSN |
2011 | null | 16876180 |
Citations | PageRank | References |
8 | 0.39 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chen Lu | 1 | 11 | 1.77 |
| Ning Ma | 2 | 8 | 0.39 |
| Zhipeng Wang | 3 | 20 | 7.49 |