Development of a smart system for diagnosing the operating conditions of a helicopter prototype via vibrations analysis

In the forward flight, wind loads affect the helicopters and cause vibration. This paper analyzes the behavior of a helicopter prototype composed by two blades when subjected to a front wind load, similar to the forwarding flight condition. An Artificial Neural Network (ANN) processes the experimental data in order to identify the pattern of its dynamic behavior. The tests led to Vibration analysis for different wind speeds. Also, the data indicates that vibration amplitude increases when the blades are subjected to the fundamental frequency and its first harmonic on tests conducted without rotor plane tilt (hover flight). On the other hand, the second test performs a 5-degree tilt on the rotor disc. In this test, the vibration amplitude decreased in the fundamental frequency, and the amplitude related to the first harmonic increased. The ANN achieved 100% efficiency in recognizing the flight conditions of the prototype.