Construction of the diagnostic model based on combining spectral characteristics of nonlinear dynamic objects

Abstract

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.