Abstract
Unbalance and asynchronous forces acting on a flexible rotor are characterized by their positions, amplitudes, frequencies and phases, using its measured vibration responses. The rotary machine dynamic model is a neural network trained with measured vibration signals previously decomposed by wavelets. A typical compaction ratio of 2048:4 is achieved in this application, considering the stationary nature of the measured vibrations signals and the shape of the chosen wavelet function. The Matching Pursuit procedure, coupled to a modified Simulated Annealing optimization algorithm is used to decompose the vibration signals. The performance of several neural network with different input database sets is analyzed to define the best network architecture in the sense to achieve successful training, minimum identification error, with maximum probability to give the correct answers. The experiments are conducted on a vertical rotor with three rigid discs mounted on a flexible shaft supported by two flexible bearings. The vibration responses are measured at the bearings and at the discs. A methodology to balance flexible rotors based on the proposed identification methodology is also presented.