Sumario: | Among all the aspects that are linked to a structural health monitoring (SHM) system, algorithms, strategies, or methods for damage
detection are currently playing an important role in improving the operational reliability of critical structures in several industrial
sectors. This paper introduces a bioinspired strategy for the detection of structural changes using an artificial immune system
(AIS) and a statistical data-driven modeling approach by means of a distributed piezoelectric active sensor network at different
actuation phases. Damage detection and classification of structural changes using ultrasonic signals are traditionally performed
using methods based on the time of flight. The approach followed in this paper is a data-based approach based on AIS, where sensor
data fusion, feature extraction, and pattern recognition are evaluated. One of the key advantages of the proposed methodology is that
the need to develop and validate a mathematical model is eliminated. The proposed methodology is applied, tested, and validated
with data collected from two sections of an aircraft skin panel. The results show that the presented methodology is able to accurately
detect damage.
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