Negative selection algorithm with constant detectors for anomaly detection

In the paper, two novel negative selection algorithms (NSAs) were proposed: FB-NSA and FFB-NSA. FBNSA has two types of detectors: constant-sized detector (CFB-NSA) and variable-sized detector (VFBNSA). The detectors of traditional NSA are generated randomly. Even for the same training samples, the position, size, and quantity of the detectors generated in each time are different. In order to eliminate the effect of training times on detectors, in the proposed approaches, detectors are generated in non-random ways. To determine the performances of the approaches, the experiments on 2-dimensional synthetic datasets, Iris dataset and ball bearing fault data were performed. Results show that FB-NSA and FFBNSA outperforms the other anomaly detection methods in most cases. Besides, CFB-NSA can detect the abnormal degree of mechanical equipment. To determine the performances of CFB-NSA, the experiments on ball bearing fault data were performed. Results show that the abnormal degree based on the CFB-NSA can be used to diagnose the different fault types with the same fault degree, and the same fault type with the different fault degree. © 2015 Elsevier B.V. All rights reserved.