Research on non-parametric prediction method of reciprocating compressor time series based on prediction credibility scale

Abstract

Aiming at the long-term unpredictability of the reciprocating compressor vibration signal, a non-parametric prediction method of reciprocating compressor time series based on the prediction credibility scale is proposed in this paper. The method is to take the multifractal singular spectrum as the prediction parameter and use the Smoothness Priors Approach (SPA) method to obtain the singular spectrum parameters of different components, and construct the phase space reconstruction dynamic modeling domains. It enables the prediction model to reflect the real-time characteristics of the dynamics evolution of complex systems and highlights the independent influence of each component on the prediction. Meanwhile, the information entropy saturation principle is introduced into the K-Nearest Neighbor (KNN) model to establish the improved K neighborhood dynamic non-parametric prediction model based on the maximum prediction credibility scale, which improves the credibility of the prediction results. Finally, a complete SPA&PSR_KNN prediction algorithm is proposed. Through example validation and error analysis, compared with KNN, BP, and SVM, it can be seen that the prediction results of spectral characteristic parameters obtained by this algorithm have smaller error and higher reliability, and faster operation speed. Thus, the prediction of vibration signal time series of reciprocating compressor is realized.