Abstract:

The non-stationary characteristics of rotating machinery equipment increase the difficulty of predicting the operating state. Therefore, based on the neural network technology, a vibration frequency time series prediction method is constructed. Combining the gradient descent method and Newton method to optimize the back-propagation neural network， aiming at the seasonality and trend of the time series of actual mechanical vibration frequency， the first-order backward difference is processed by the difference method， the autoregressive sequence is deduced， and the time series prediction model of rotating machinery vibration frequency is obtained. In the experimental link， the vibration frequency time series within one hour is predicted for the rotor of a steam turbine generator unit in a power plant， and the experiment is completed on the basis of setting the parameters such as the number of network layers. From the absolute error and relative error values， the proposed method has the ability to reflect the vibration frequency trend， and the prediction accuracy is ideal.

Key words: electromechanical engineering, back propagation neural network, rotating machinery, vibration frequency, time series, objective function gradient