Research Article A Bioinspired Methodology Based on an Artificial Immune System for Damage Detection in Structural Health Monitoring Maribel Anaya, 1,2 Diego A. Tibaduiza, 2 and Francesc Pozo 3 1 CoDAlab, Department of Applied Mathematics III, Universitat Polit` ecnica de Catalunya (UPC), 08036 Barcelona, Spain 2 Faculty of Electronic Engineering, Universidad Santo Tom´ as, Bogot´ a, Colombia 3 CoDAlab, Department of Applied Mathematics III, Escola Universit` aria d’Enginyeria T` ecnica Industrial de Barcelona (EUETIB), Universitat Polit` ecnica de Catalunya (UPC), Comte d’Urgell 187, 08036 Barcelona, Spain Correspondence should be addressed to Francesc Pozo; francesc.pozo@upc.edu Received 27 February 2015; Revised 5 May 2015; Accepted 14 May 2015 Academic Editor: Haifeng Gao Copyright © 2015 Maribel Anaya et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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. Tis paper introduces a bioinspired strategy for the detection of structural changes using an artifcial immune system (AIS) and a statistical data-driven modeling approach by means of a distributed piezoelectric active sensor network at diferent actuation phases. Damage detection and classifcation of structural changes using ultrasonic signals are traditionally performed using methods based on the time of fight. Te 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. Te proposed methodology is applied, tested, and validated with data collected from two sections of an aircraf skin panel. Te results show that the presented methodology is able to accurately detect damage. 1. Introduction Structural health monitoring (SHM) is a discipline that makes use of sensors permanently attached to a structure together with diferent sofware analysis developments in order to detect damage and assess the proper performance of structures. An SHM system traditionally includes continuous monitoring, data processing algorithms, and pattern recogni- tion techniques for a robust analysis. Diferent methodologies have been developed in the last years in the feld of SHM. However, with the use of bioinspired algorithms, promising results have been obtained, mainly due to its adaptive, distributed, and autonomous features. Tis work presents a damage detection methodology that is mainly based on an artifcial immune system (AIS) as a pattern recognition technique and afnity plots to discrim- inate the diferent structural states of the structure. Tis methodology is applied to the collected data by a piezoelectric system. Te artifcial immune system has been proposed and used in several applications. However, in structural health monitoring, this methodology is relatively new. A brief state of the art in structural health monitoring is presented, in chronological order, in the next lines, highlighting the most representative works with respect to artifcial immune systems. Te use of nondestructive testing inspection methods (NDT) has proved to be a very useful tool for damage detec- tion tasks. However, in some situations where it is impos- sible to manually inspect a structure, as in the inspection of large-scale structures, the use of automated methods presents signifcant advantages. Some of these advantages can be summarized as follows: (i) continuous monitoring, since the sensors are permanently attached to the structure; (ii) early damage detection; and (iii) damage identifcation, among others. In this sense, structural health monitoring (SHM) extends the limits of the NDT methods by including Hindawi Publishing Corporation Shock and Vibration Article ID 648097