Validation of artificial neural networks to model the acoustic behaviour
  of induction motors
F. J. Jimenez-Romero D. Guijo-Rubio F. R. Lara-Raya A. Ruiz-Gonzalez C. Hervas-Martinez
In the last decade, the sound quality of electric induction motors is a hot
topic in the research field. Specially, due to its high number of applications,
the population is exposed to physical and psychological discomfort caused by
the noise emission. Therefore, it is necessary to minimise its psychological
impact on the population. In this way, the main goal of this work is to
evaluate the use of multitask artificial neural networks as a modelling
technique for simultaneously predicting psychoacoustic parameters of induction
motors. Several inputs are used, such as, the electrical magnitudes of the
motor power signal and the number of poles, instead of separating the noise of
the electric motor from the environmental noise. Two different kind of
artificial neural networks are proposed to evaluate the acoustic quality of
induction motors, by using the equivalent sound pressure, the loudness, the
roughness and the sharpness as outputs. Concretely, two different topologies
have been considered: simple models and more complex models. The former are
more interpretable, while the later lead to higher accuracy at the cost of
hiding the cause-effect relationship. Focusing on the simple interpretable
models, product unit neural networks achieved the best results: for MSE and for
SEP. The main benefit of this product unit model is its simplicity, since only
10 inputs variables are used, outlining the effective transfer mechanism of
multitask artificial neural networks to extract common features of multiple
tasks. Finally, a deep analysis of the acoustic quality of induction motors in
done using the best product unit neural networks.