Statistical pattern recognition based structural health monitoring strategies

Structural Health Monitoring (SHM) is concerned with the analysis of aerospace, mechanical and civil systems with the objective of identifying damage at its onset. In civil engineering applications, damage may be defined as any change in the structural properties that hinders the current or future performance of that system. This is the premise on which vibration-based techniques are based. Vibration-based methods exploit the response measured directly on the system to solve the SHM assignment. However, also fluctuations in the external conditions may induce changes in the structural properties. For these reasons, the SHM problem is ideally suited to be solved within the context of statistical pattern recognition, which is the discipline concerned with the automatic classification of objects into categories. Within the statistical pattern recognition based SHM framework, the structural response is portrayed by means of a compact representation of its main traits, called damage sensitive features (dsf). In this dissertation, two typologies of dsf are studied: the first type is extracted from the response of the system by means of digital signal processes alone, while the other is obtained by making use of a physical model of the system. In both approaches, the effects of external conditions are accounted for by modeling the damage sensitive features as random variables. While the first method uses outlier analysis tools and delivers a method optimally apt to perform the task of damage detection within the short-term horizon, the second approach, being model-based, allows for a deeper characterization of damage, and it is then more suited for long-term monitoring purposes. In the dissertation, an approach is also proposed that allows the use of the statistical pattern recognition framework when there is limited availability of data to model the damage sensitive features. All proposed methodologies are validated both numerically and experimentally