An Artificial Immune System Approach for the Fault Detection and Diagnosis of a DC Machine

In this paper, an artificial immune system approach for the detection and diagnosis of faults in the dc machines is presented. The proposed technique requires the measurement of two output variables to compute their representation before and after a fault condition. A pattern recognition algorithm inspired by how the immune system operates throughout the body is proposed to identify and classify the fault condition. According to the proposed methodology, there is no need to know the details of machine operation in a certain regime and all phenomena and effects resulting from the machine operating in this regime are taken into account. Experimental results obtained on 5HP 240V 1750RPM dc machine is presented and discussed to validate the methodology, verifying its good performance in preventive fault detection.