Monitoring structural integrity in dynamic rotors using artificial intelligence with continuous learning
DOI:
https://doi.org/10.33448/rsd-v12i6.42017Keywords:
Structural integrity monitoring; Dynamic rotors; Artificial immune systems; Negative selection algorithm; Clonal selection algorithm; Continuing learning.Abstract
This paper aims to develop an artificial intelligence algorithm with continuous learning, inspired by the functioning of the biological immune system, to monitor structural integrity. This intelligent algorithm will be developed using techniques based on artificial immune systems (AIS), such as the negative selection algorithm (NSA) and the clonal selection algorithm (ASC). SIAs are promising techniques in the field of artificial intelligence, and their design is inspired by biological immune systems, with the aim of computationally reproducing their main characteristics, properties, functionalities and abilities. Artificial immune systems are powerful tools suitable for performing complex tasks, such as diagnoses, as a result of having the ability to identify behavioral changes as their main ability. Because it is a stable, reliable and adaptive architecture, it allows the inclusion of the continuous learning module, which allows the system to absorb new experiences and knowledge without the need to restart the memory cells (knowledge). In this way, this system will become more efficient as new information is processed and made available. In other words, it is an intelligent system that seeks improvement over time. This property is an advantage of SIAs, and it should be highlighted because, unlike what happens with other artificial intelligence techniques, such as, for example, artificial neural networks (ANN), SIAs allow the possibility of continuous learning.
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