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2018
Conference Paper
Titel
Structural health monitoring of composite aerospace structures with acoustic emission
Abstract
While composite materials have many advantages due to their lightweight potential impact events or Foreign Object Damage (FOD) is critical for composite and many other lightweight and thin aerospace structures. FOD can lead to unscheduled maintenance in the cases of hail damage, tool drop, ramprash or even loss in the cases of tire debris (Concorde Accident July 25th, 2000) or insulation debris (Columbia Space Shuttle Feb. 1st, 2003). Impact damage reduces the static load capability and the fatigue life of a structure. The cost of an aircraft on ground is about 40 k$ per hour. Thus a decision on unscheduled inspection and repair or a return to flight must be taken fast and responsibly. Nondestructive inspection combined with numerical analysis is the state of the art. A detailed numerical analysis of predamaged parts may take several days or weeks. Novel fast Structural Health Monitoring(SHM) and predictive maintenance tools can support the necessary decision making process. Impact damage generates characteristic acoustic signals that can be detected and analyzed by acoustic emission systems during the event. This was investigated in the Clean Sky Program in the Green Regioanl (GRA) platform* by Fraunhofer LBF (Laboratory for ""Betriebsfestigkeit"" - structural durability). A fast but simple analytical model was developed that can analyze certain extracted acoustic features. This model was trained with 50 composite plates clamped to simulate a stringer bayeach. The specimen were subjected to different impact energies and locations and corresponding Acoustic Emission (AE) features, damage sizes as well as the compression load after impact were derived from these tests. After this training the system could analyze impact events in near-real-time and present estimations on impact energy levels, location, damage size, mechanical properties, delamination growth as well as the remaining fatigue life under a given load level. The project closed the loop from data acquisition with a commercial AE system, via the assessment of the structural properties based on sensor records to the prognosis of this structural health and its presentation in near real-time after impact events. Unfortunately the high scatter of results regarding impact testing as well as of the extracted acoustic signals affects the reliability of the system so far but with approaches from big data methods AE may become an interesting sensor type for predictivemaintenance of sporadic failure types.
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