Construction of the nonlinear dynamic objects diagnostic model based on of multiple factors variance analysis

Abstract

In this work, the problem of diagnostic models constructing under conditions of description dimension increase in the modern diagnostic objects solves. As a diagnostic objects considers the nonlinear dynamics objects with continuous characteristics and an unknown structure, which can be considered as a “black box”. The purpose of the work is to increase the reliability of the diagnosis of nonlinear dynamic objects by forming diagnostic models under conditions of the objects description dimensionality increasing. A review of methods for reducing the dimensionality of the diagnostic features space is given. A method for the construction of diagnostic models of nonlinear dynamic objects with weak nonlinearity on the basis of univariate and multivariate analysis of variance as a filtering stage of signs is proposed. A step-by-step algorithm for the construction of diagnostic models using the proposed method is presented. On the example of the task of technical diagnosis a jet engine, diagnostic models are constructed on the basis of univariate and multivariate analysis of variance of continuous models. A family of diagnostic models of a jet engine is proposed.