Identification of hybrid ARX-neural network models for three-dimensional simulation of a vibroacoustic system

Abstract

Acoustic noise in industrial areas, typically generated by compressors and vacuum pumps, may be mitigated by the combined use of passive and active noise control strategies. Despite its widespread use, the traditional Active Noise Control (ANC) technique requires error feedback and has been proven to be effective only within a small spatial region. When the movement of human ears is required within a large region and error feedback is difficult to be accomplished, new cancelling strategies have to be devised to achieve acceptable levels of spatial coverage. In the pursuit of this goal, this paper proposes a vibroacustic model to predict noise radiated from machinery. The model output is the sound signal of the noise at a given point inside a closed room. The two model inputs are the vibration signal at the noise source and the spatial coordinates of the intended point. Experimental output data were measured at several points inside a region defined by a solid rectangle. A fixed-order ARX model was chosen (AutoRegressive with eXogenous input), and for each spatial point and its corresponding pair of input–output signals, a set of parameter values was estimated. To integrate all these models into a single one, a neural network was employed to associate or approximate each set of parameters to its spatial coordinates. With this approach, the total number of parameters is expected to be greatly reduced, when considering the original separated models. Experimental results are presented and comparisons with other models are established on the basis of least-square error metrics and parsimony of parameters. A qualitative perspective for employing the proposed model in the design of large-region ANC strategies is also offered.