Modelling and forecasting of quasi-periodic processes in technical objects based on cylindrical image models

Abstract

Accurate forecasting of the state of technical objects is necessary for effective management. The technical condition of the object is characterized by a system of time series of monitored indicators. The time series often have difficultly predictable irregular periodicity (quasi-periodicity). In this paper, to improve the accuracy of such series forecasting, models of quasi-periodic processes in the form of samples of a cylindrical image are used. The application of these models is demonstrated by forecasting of a hydraulic unit vibrations. It is shown that the use of these models provides a higher accuracy of prediction compared with the classical approaches. Quasi-periodic process as a scan of an imageDouble correlation is a characteristic property of a quasi-periodic process, that is, there is a strong correlation both between adjacent values and between values separated by several periods.There are a number of approaches to describe quasi-periodicity: the imposition of noise or higher frequencies on the main harmonic, periodic nonstationarity (fluctuation of moments and other characteristics), and so on. A common manifestation of such representations is the high correlation of process values at distances that are multiples of a period. In this paper,this property is