An Improved Negative Selection Algorithm-Based Fault Detection Method

Abstract

Fault detection has been an active research field and increasingly important for the safety of technical processes and systems. A variety of fault detection methods have been developed that are generally designed for a specific system; therefore, they target limited types of fault. An efficient approach that could monitor the degradation conditions and be less problem-specific is necessary for improved reliability and efficiency. This paper presents an improved negative selection algorithm using specialized detectors that is model-free and independent of prior knowledge about fault types. An artificial immune system employs a negative selection algorithm and requires only normal (self) patterns for detector generation. In the training phase, uncovered gaps are identified and covered with new detectors to improve nonself (faulty) space coverage. Moreover, to alleviate the online detection, cost reshaping process of nonself space with detector clusters is performed. Theoretical analysis shows that the nonself space coverage with a specialized detector is effectively improved. In experimental analysis, three data sets are used for training and testing, including two-dimensional patterns (cross-mid, ring-mid), Fisher Iris, and KDD CUP99. The experimental results show that the accuracy of the developed method is better than that of the standard negative selection algorithm and other established machine learning algorithms with reduced online anomaly detection time.