Sensor Placement Optimization for SHM Systems Under Uncertainty

Structural Health Monitoring (SHM) systems that report in real-time a flight vehicle's condition are central to meeting the goals of increasing flight vehicle safety and reliability, while reducing operating and maintenance costs. The structural response of flight vehicles is inherently random, requiring deterministic finite element analyses to be augmented with uncertainty quantification methods to compute the response statistics and structural damage probability. To detect damage with maximum probability, sensors must be placed optimally. This requires combining a probabilistic finite element method (FEM) with damage detection algorithms and optimization techniques. This study develops a methodology to integrate the above disciplines into a sensor placement optimization (SPO) methodology for SHM systems under uncertainty. To achieve this, the structural component under consideration is analyzed via FEM and uncertainty of model input quantities is included in the analysis via random processes and fields. In the next two steps probabilistic FEM analyses are performed to determine the model output variability and using these results, damage detection procedures such as feature extraction and state classification are applied to assess the current structural state of the component. Repeating these two steps using both healthy and damaged structural models helps quantify the reliability of a given sensor layout. Finally, SPO is achieved to maximize the reliability of damage detection. The sensor layout design of a thermal protection system component is used as a numerical example.