Layered and Real-Valued Negative Selection Algorithm for Fault Detection

In this paper, the challenging task of the development of a generic fault detection (FD) method is addressed. While the past FD research has primarily focused on modeling and signal-processing methods that are problem specific and require complete knowledge of the system model and fault types, this paper presents a novel layered and real-valued negative-selection algorithm (LRNSA)-based FD method independent of prior knowledge of fault types and patterns. Specifically, in the training phase, the nonself-space is divided into different layers for effective generation and distribution of detectors using normal (self) data. The major accomplishments of the proposed method are improved nonself-space coverage of the uncovered gaps (holes), followed by the formation of cluster detector with large radius. To test the capabilities of the developed method, the generated specialized detector distribution is studied using bearing fault modeling in a three-phase induction motor. The proposed method is subsequently investigated and validated by applying it to an actual induction motor for different types of bearing faults. Finally, the comparative results on the benchmark dataset demonstrate the superiority of the proposed method compared to the state-of-the-art machine learning algorithms in terms of higher FD accuracy and quick detection with reduced online detection time.