An Evolutionary Immune Network for Data Clustering
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
Fundamentals of Natural Computing (Chapman & Hall/Crc Computer and Information Sciences)
Fundamentals of Natural Computing (Chapman & Hall/Crc Computer and Information Sciences)
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
Run-time malware detection based on positive selection
Journal in Computer Virology
Expert Systems with Applications: An International Journal
A transitional view of immune inspired techniques for anomaly detection
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Journal of Intelligent Manufacturing
Negative selection algorithm based on grid file of the feature space
Knowledge-Based Systems
Hi-index | 12.05 |
This paper presents a methodology that designs a fault detection Artificial Immune System (AIS) based on immune theory. The fault detection is a challenging problem due to increasing complexity of processes and agility necessary to avoid malfunction or accidents. The key fault detection challenge is determining the difference between normal and potential harmful activities. A promising solution is emerging in the form of AIS. The SelfxNonself theory inspired an immune-based fault detection approach. This article proposes the AIS Multi-Operational Algorithm based on the Negative Selection Algorithm. The proposed algorithm is used to a DC motor fault model benchmark to compare its relative performance to others fault detection algorithms. The results show that the strategy developed is promising for incipient and abrupt fault detection.