Abstract
This paper presents a fault detection and identification (FDI) approach inspired by the immune system. The salient features of the immune system, such as adaptability, robustness, flexibility, archival memory and distributed cognition abilities, have been the valuable source of inspiration for fundamentally new methods for fault detection and identification. This research makes use of immunological concepts to develop a robust fault detection and identification mechanism, capable of detecting and classifying diverse system faults dynamically. Such an FDI mechanism also has the ability to learn and classify overlapping faults using distributed sensing. Moreover, its detection accuracy can be continuously improved during system operation. As tested by numerical simulations in which faults are represented by overlapping banana functions, the proposed algorithms are adaptive to new types of faults and overlapping faults.
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