Scaled conjugate gradient and Bayesian training of neural networks for fault identification in cylinders
This paper compares Bayesian training of neural networks using hybrid Monte Carlo to scaled conjugate gradient method for fault identification in cylinders using vibration data. From the measured data pseudo-modal energies and modal properties are calculated and the coordinate pseudo-modal energy assurance criterion (COMEAC) and the coordinate modal assurance criterion (COMAC) are computed respectively. The pseudo-modal energies, modal properties, COMEAC and COMAC are used to train four neural networks. On average, the pseudo-modal-energy-networks are more accurate than the modal-property-networks. The weighted averages of the pseudo-modal-energy- and modal-property-networks form a committee of networks. The committee method gives lower mean squared errors and better classification of faults than the individual methods. The Bayesian training is found to be more accurate and computationally expensive than the scaled conjugate gradient method and to give confidence levels.
bayesian neural networks fault identification vibration maximum-likelihood