Design and validation of a structural health monitoring system for aeronautical structures.

Structural Health Monitoring (SHM) is an area where the main objective is the verification of the state or the health of the structures in order to ensure proper performance and maintenance cost savings using a sensor network attached to the structure, continuous monitoring and algorithms. Different benefits are derived from the implementation of SHM, some of them are: knowledge about the behavior of the structure under different loads and different environmental changes, knowledge of the current state in order to verify the integrity of the structure and determine whether a structure can work properly or whether it needs to be maintained or replaced and, therefore, to reduce maintenance costs. The paradigm of damage identification (comparison between the data collected from the structure without damages and the current structure in orderto determine if there are any changes) can be tackled as a pattern recognition problem. Some statistical techniques as Principal Component Analysis (PCA) or Independent Component Analysis (ICA) are very useful for this purpose because they allow obtaining the most relevant information from a large amount of variables. This thesis uses an active piezoelectric system to develop statistical data driven approaches for the detection, localization and classification of damages in structures. This active piezoelectric system is permanently attached to the surface of the structure under test in order to apply vibrational excitations and sensing the dynamical responses propagated through the structure at different points. As pattern recognition technique, PCA is used to perform the main task of the proposed methodology: to build a base-line model of the structure without damage and subsequentlyto compare the data from the current structure (under test) with this model. Moreover, different damage indices are calculated to detect abnormalities in the structure under test. Besides, the localization of the damage can be determined by means of the contribution of each sensor to each index. This contribution is calculated by several different methods and their comparison is performed. To classify different damages, the damage detection methodology is extended using a Self-Organizing Map (SOM), which is properly trained and validated to build a pattern baseline model using projections of the data onto the PCAmodel and damage detection indices. This baseline is further used as a reference for blind diagnosis tests of structures. Additionally, PCA is replaced by ICAas pattern recognition technique. A comparison between the two methodologies is performed highlighting advantages and disadvantages. In order to study the performance of the damage classification methodology under different scenarios, the methodology is tested using data from a structure under several different temperatures. The methodologies developed in this work are tested and validated using different structures, in particular an aircraft turbine blade, an aircraft wing skeleton, an aircraft fuselage,some aluminium plates and some composite matarials plates.