Design of an Artificial Immune System for fault detection: A Negative Selection Approach

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 Self × Nonself 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.