Rotary machine degradation trend prediction method based on steady subspace exogenous vector autoregression

Abstract

The invention discloses a rotary machine degradation trend prediction method based on steady subspace exogenous vector autoregression, which comprises the following steps of: firstly, carrying out first stationary subspace decomposition on denoised multi-channel signals to extract vibration stationary components; extracting time-domain and frequency-domain degradation characteristic quantities andobtaining a high-dimensional degradation index vector group through characteristic fusion; performing second stationary subspace decomposition and differential operation on the high-dimensional degradation index vector group in the time domain and the frequency domain to extract weak stationary components in the degradation indexes as rotary machine degradation indexes; carrying out stability test and impulse response analysis on the degradation indexes, determining endogenous and exogenous variables and model orders, determining vector autoregression model parameters through maximum likelihood estimation, and finally carrying out degradation trend estimation on the rotary machine at different prediction starting points. The degradation trend prediction model obtained by the method not only has good generalization ability under small sample learning, but also is rapid in calculation and strong in releasability.

Description

Technical field [0001] The invention relates to the technical field of degradation trend prediction in rotating machinery and equipment, and is a method for predicting the degradation trend of rotating machinery based on autoregressive vector autoregression in a stationary subspace. Specifically, it extracts weak stationary through quadratic stationary subspace decomposition and differential operation Vibration degradation index and a method for predicting the degradation trend through external vector autoregression. Background technique [0002] The continuous aging and increasing demand in the operation of rotating machinery call for more advanced fault prediction and health management technology, among which degradation trend prediction plays a vital role in the complex engineering system of assembling rotating machinery. Accurate degradation trend prediction methods can provide machine status information and health status in advance for predictive maintenance, thereby avoidin...

AI Technical Summary

Problems solved by technology

It effectively overcomes the current problems of weak generalization ability, long calculation time and "black box effect" in the prediction of the degradation trend of rotating machinery under small samples, and has important economic and social value

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