Promoting Novel Strategies for the Reliability Assessment of Guided Wave Based SHM Systems

Detection of defects is of outmost importance for composite airframe due to barely visible damage which may appear at any time during lifetime. Several Structural Health Monitoring (SHM) technologies have been developed so far to continuously monitor the condition of the structure. Guided Ultrasonic Waves (GUW) are well suited for this application, but there is still a significant gap in reliability assessment and quantification of system performances. Although the theory of Probability of Detection (POD) can be used to this end, but the reliability assessment procedure needs to be properly addressed for each type of system. In this work different concepts are proposed, including two artificial intelligence approaches, tested and compared to enable a reliability assessment comparable to the conventional POD framework. On top, a novel approach on how to combine experimental data of the undamaged structure with simulations of the damaged structure is proposed. The proposed ideas are tested on a single carbon fiber composite specimen, and data is analyzed using all the different concepts. The results show that classic POD and AI-based reliability assessment can be compared as well as experimental and synthetic data can be used when the experimental variability is matched properly providing a paradigm shift in the reliability assessment field.