In the framework of the transition toward condition-based maintenance, connected objects deployment is reinventing monitoring processes. This is extremely relevant with respect to corrosion which represents a significant issue in aeronautics. In 2016 the annual corrosion cost for commercial aircraft fleet operated by European airlines was estimated to 2.2 B$. Anticipating corrosive conditions ahead of time is estimated to generate between 15% and 35% of cost savings. Specific coatings are used to prevent corrosion but remain limited in their ability to completely avoid structural corrosion specially in harsh operational environment. To detect such damages, nondestructive testing methodologies for corrosion consist in “on ground” regular visual inspection which does not allow detection of the corrosion damage premises, precise quantification of corrosion damage size, and prediction of the remaining useful life of associated parts. Adding on board native connectivity to aircraft metallic parts appears as the key technology to safely face this issue while minimizing costs and environmental impact. This goes through embedding ultrasonic sensors into aeronautic airframe parts and providing them with on board structural health monitoring algorithms and digital twins able to improve operational availability without compromising safety. COQTEL is the joint multidisciplinary effort of French experts in structural health monitoring (PIMM lab), ultrasonic hardware designing (CTEC SME), aerospace metallic parts coating (RESCOLL SME), and corrosion and fatigue modelling (I2M lab) to face this challenge. It proposes to move from classical “on ground” non-destructive testing to “on board” condition-based maintenance by embedding ultrasonic sensors in aircraft metallic parts and by developing and validating dedicated hardware, digital twins, and algorithms endowing airframe parts with built-in corrosion monitoring functionalities. The ambition of COQTEL is to detect the premises of corrosion, to quantify the size of in situ corrosion damages, and to be able to predict associated remaining useful life in order to participate in the revival of the aeronautical industry and the SMEs accompanying it by providing them with a proof of concept of the use of intelligent aeronautical structures for predictive maintenance.