An artificial immune systems approach to case-Based Reasoning applied to fault detection and diagnosis.

Abstract This work presents a hybrid model of case-based reasoning (CBR) and artificial immune systems (AIS), which is able to manage the processes of recovery, adaptation (reuse and revision) and retention of cases. The developed model also provides an alternative way of clustering cases, identifying high density areas, improve search efficiency in the case space and store relationships among similar cases. The proposed model is applied to a fault detection and diagnosis problem of direct currnet motor nenchmark and the obtained results are compared using specific CBR performance metrics showing promising perspectives for the proposed model.