Name
Title | ||
|---|---|---|
ART-Artificial immune network and application in fault diagnosis of the reciprocating compressor |
Abstract | ||
|---|---|---|
Inspired by complementary strategies, a new fault diagnostic method, which integrates with the Adaptive Resonance Theory (ART) and Artificial Immune Network (AIN), is proposed in this paper. With the help of clustering of ART neural network, the vaccines that image features of data set are extracted effectively, and then an AIN named aiNet is adopted to realize data compression. Finally the memory antibodies optimized by aiNet can be used to recognize each feature of original dataset and to realize fault diagnosis. The experimental results show that the approach is useful and efficient for the fault diagnosis of the multilevel reciprocating compressor. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11881223_62 | ICNC (2) |
Keywords | Field | DocType |
fault diagnosis,complementary strategy,new fault,image feature,reciprocating compressor,data compression,art neural network,diagnostic method,art-artificial immune network,adaptive resonance theory,artificial immune network,neural network,image features | Adaptive resonance theory,Artificial immune system,Immune network,Computer science,Feature (computer vision),Artificial intelligence,Reciprocating compressor,Artificial neural network,Cluster analysis,Data compression,Machine learning | Conference |
ISBN | Citations | PageRank |
3-540-45907-3 | 0 | 0.34 |
References | Authors | |
3 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mao-Lin Li | 1 | 9 | 2.24 |
| Na Wang | 2 | 0 | 0.34 |
| Haifeng Du | 3 | 421 | 30.96 |
| Jian Zhuang | 4 | 104 | 15.09 |
| Sun'an Wang | 5 | 34 | 5.40 |