Artificial immune system for fault detection and localization in a composite material plate with temperature variation

Modern society is strongly reliant on structural and mechanical systems, and the usage of composite materials in this context has grown dramatically in recent decades. Because of some better qualities over metallic materials, they have been driven by various industry sectors. However, composites often have complex and anisotropic internal structures, which eventually lead to various types of damage, making possible structural failures difficult to diagnose and anticipate. In this context, structural health monitoring (SHM) refers to the process of identifying damage to engineering structures. One of the proposed solutions for SHM is so-called artificial immune systems (AISs), which replicate the human immune system, and this is a field of study that integrates immunology, computer science, and engineering to address complicated computational problems. As a result, the goal of this work is to implement an SHM approach for damage identification and localization based on impedance data from a composite material plate subjected to temperature variations and progressive damage growth. An optimized methodology involving signal analysis in the time domain was achieved through reading and processing based on the label of signals, which presented an F1-score equal to 1.0 and a 100% probability of damage detection is even capable of locating the path in which the damage is inserted. As a result of its average processing speed of 4.8 s and substantial memory capacity, an application for continuous monitoring of composite structures subjected to temperature variations was developed.