The task of constructing diagnostic models for nonlinear dynamics objects solved in this work. The reasons for increasing the dimension of the modern diagnostics objects description and related problems of using existing diagnostics methods are considered. The purpose of this work is to increase the accuracy and reliability of nonlinear dynamic objects diagnosing by forming diagnostic models in the conditions of increasing the dimension of the objects description for creating effective tools for automated systems of technical diagnostics. It is offered a broad overview and classification of methods for reducing the dimension space of diagnostic features including nonlinear dynamic objects with continuous characteristics and unknown structure, which can be considered as a “black box”. The forming diagnostic models method of nonlinear dynamic objects based on the combination of spectral characteristics obtained as the result of continuous models transformations: wavelet transformations coefficients and models moments of different orders is proposed. The family of diagnostic models is proposed as combinations of dynamic objects spectral characteristics with weak nonlinearity. The hybrid method of forming diagnostic models based on the combination of spectral characteristics suggested. The method consist of sequential application of feature filtering for forming primary feature space, construction of secondary feature space using the spectral transformations and diagnostic model construction by complete bust of secondary features. It is developed a detailed algorithm for constructing diagnostic models using the proposed hybrid method. The suggested method has been tested on real-life task of diagnosing a non-linear dynamic object – a electric motor. Primary diagnostic model of the electric motor taken on the base of indirect measurements of the air gap between the rotor and the stator of the motor. Diagnostic models constructed by combining the spectral characteristics of continuous models. The diagnostic models family of the switched reluctance motor is offered. The method is demonstrate more independence of the accessibility indicator then existing methods of the diagnostic feature space biulding: the samples, the moment and the coefficients of wavelet transformations of the primary diagnostic models.

In this work, the problem of diagnostic models constructing under conditions of description dimension increase in the modern diagnostic objectssolves. As a diagnostic objects considers the nonlinear dynamics objects with continuous characteristicsand an unknown structure, which can be considered as a “black box”. The purposeof the work is to increase the reliability of the diagnosis of nonlinear dynamic objects by forming diagnostic models under conditionsof the objects description dimensionality increasing. A review of methods for reducing the dimensionality of the diagnostic features space is given. A method for the constructionof 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 constructionof 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 univariateand multivariate analysis of variance of continuous models. A family of diagnostic models of a jet engine is proposed.