Low-velocity impacts, like those due to stone debris, hailstone and bird strike, are a present issue for helicopter operations. Aim of the paper is to develop a low-velocity impact monitoring system able to provide information regarding the position of a low velocity impact. Impacts have been implemented by means of a dynamometric hammer on sandwich panels representing a helicopter frame. One of the most innovative aspects of the research regards the use of a sensor network composed of strain gauges, which are rarely applied for impact localization purposes. Several experimental impact tests have been carried out and an artificial neural network (ANN) has been trained. The system demonstrates the possibility to apply the sensors on a face of the sandwich panel whilst the impacts take place on the other, still guaranteeing robust impact localization. Furthermore, if proper filtering techniques are applied, the algorithm provides a general response regardless of the material used as the hammer tip.

Low-velocity impact monitoring system for a helicopter frame by means of an artificial neural network

SBARUFATTI, CLAUDIO;GILIOLI, ANDREA;MANES, ANDREA;GIGLIO, MARCO
2015

Abstract

Low-velocity impacts, like those due to stone debris, hailstone and bird strike, are a present issue for helicopter operations. Aim of the paper is to develop a low-velocity impact monitoring system able to provide information regarding the position of a low velocity impact. Impacts have been implemented by means of a dynamometric hammer on sandwich panels representing a helicopter frame. One of the most innovative aspects of the research regards the use of a sensor network composed of strain gauges, which are rarely applied for impact localization purposes. Several experimental impact tests have been carried out and an artificial neural network (ANN) has been trained. The system demonstrates the possibility to apply the sensors on a face of the sandwich panel whilst the impacts take place on the other, still guaranteeing robust impact localization. Furthermore, if proper filtering techniques are applied, the algorithm provides a general response regardless of the material used as the hammer tip.
Annual Forum Proceedings - AHS International
Hammers; Helicopter services; Helicopters; Honeycomb structures; Neural networks; Sandwich structures; Sensor networks; Structural panels; Velocity Bird strike; Filtering technique; Helicopter operation; Low velocity impact; Sandwich panel
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/982790
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