ART-Artificial Immune Network and Application in Fault Diagnosis of the Reciprocating Compressor
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.
KeywordsArtificial Immune System Immune Network Adaptive Resonance Theory Suppression Threshold Reciprocating Compressor
Unable to display preview. Download preview PDF.
- 2.Dunbar, G.: The Clustering of Natural Terms: an Adaptive Resonance Theory Model. In: IEEE International Joint Conference on Neural Network, vol. 6, pp. 4362–4364 (1999)Google Scholar
- 5.Castro, L.N., Zuben, F.J.: aiNet: An Artificial Immune Network for Data Analysis. Idea Group Publishing, Pennsylvania (2001)Google Scholar
- 6.Wang, J., Wang, R., Miao, D.Q., et al.: Data Enriching Based on Rough Set Theory. Chinese Journal of Computers 21, 393–400 (1998)Google Scholar