Data fusion for the efficient NDT of challenging aerospace structures: a review

High performance multifunctional structural components and other system components are evolving for applications in the aerospace industry. The efficient operation and reliability of these structures must be ensured by suitable means for inspection and maintenance. However, inspection on complex structural elements via traditional non-destructive testing (NDT) methods presents challenges for the accurate detection and characterization of flaws. The combination of NDT methods offers considerable advantages over existing NDT technologies and ensures not only accuracy in the inspection, but also another perspective on flaws which otherwise would not have been identified. Large sets of data are generated through these inspections and require robust data fusion technologies for visualisation and interpretation. Emerging technologies such as artificial intelligence, internet of things and automation direct towards the new paradigms NDT 4.0 and digital twin. The measurement data for assessing the health and faulty conditions of the structure using integrated sensors and NDT methods can be represented in a digital replication of the structure called the digital twin. Technologies such as drones, augmented reality and remote NDT can help to improve the efficiency of inspections. Therefore, this review represents current technologies and concepts for NDT 4.0 and the digital twin concepts which are suitable to save time, optimize processes and maintenance costs