Fault diagnosis based on the artificial immune algorithm and negative selection

Modern manufacturing techniques depend upon systems that produce high volumes with consistent quality in order to ensure maximum productivity. One source of reduced productivity is equipment failure. To minimize these production losses, we propose an intelligent system that is incorporated into the architecture of a machine for detecting the onset of equipment malfunctioning, and to generate corrective action. The intelligent diagnostic system is based upon the artificial immune algorithm and the technique of negative selection. The proposed immune based system monitors a machine´s transition states during an operating cycle and immediately detects the occurrence of an anomaly.