Name
Title | ||
|---|---|---|
The Detection System of Oil Tube Defect Based on Multisensor Data Fusion by Improved Simulated Annealing Neural Network |
Abstract | ||
|---|---|---|
A detection system of oil tube defect based upon improved simulated annealing neural network is presented, it got the original information by multigroup vortex sensors and leakage magnetic sensors. We made multiscale wavelet transform and frequency analysis to multichannels original data and extracted multi-attribute parameters from time domain and frequency domain, then we selected the key attribute parameters that have bigger correlativity with the defect pattern of oil tube among of multi-attribute parameters. The oil tube defect pattern had four class that is crack, etch pits, eccentric wear and unbroken. The improved simulated Annealing artificial neural network was adopt to make the multisensor data fusion to detect the defect pattern of oil tube and those key attribute parameters were used to as input of network. The experimental results show that this method is feasible and effective. |
Year | Venue | Keywords |
|---|---|---|
2007 | Lecture Notes in Engineering and Computer Science | multisensor,data fusion,defect detection,simulated annealing neural network,oil tube |
Field | DocType | ISSN |
Simulated annealing,Computer science,Sensor fusion,Artificial intelligence,Artificial neural network,Machine learning | Conference | 2078-0958 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Meijuan Gao | 1 | 32 | 12.32 |
| Jingwen Tian | 2 | 36 | 13.10 |
| Kai Li | 3 | 3 | 3.30 |
| Hao Zhou | 4 | 1 | 2.10 |