Novelty detection across a small population of real structures: A negative selection approach

Vibration-based Structural Health Monitoring (SHM), exploits a variety of approaches for novelty detection. In particular, many data-based methods try to recognise patterns by exploiting analogies with the human body's natural defences at a cellular level. These algorithms fall within the Artificial Immune System (AIS) class and can be chosen, according to their peculiarities, to solve specific problems in diverse application areas. This study investigates the damage-detection process in different operational conditions, obtained by applying structural modifications to a laboratory-scale aeroplane, which follows the geometric features of the GARTEUR benchmark project. Damage identification is performed by exploiting the Negative Selection Algorithm (NSA), already applied by some of the authors on numerically-simulated case studies, and chosen for its capability of self/non-self discrimination under varying operational or environmental conditions. The research is expanded by using sparse autoencoders for feature dimensionality reduction. The method is applied to an experimental dataset acquired via Scanning Laser Doppler Vibrometer (SLDV) measurements, to identify consistent damage-sensitive features from the frequency response functions, and to obtain a reliable fault-detection performance.