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.

Detecting development scenarios of dynamic events in electric power networks smart-grid Part1 “Method”

“…Further, we will consider dynamic OCPs during the development of short-term semantic events in the changing internal structure of OCPs (Fig. 1) [5].…”

“…This property of the OCP during operation can lead to instability of the RPA devices, and, consequently, the entire system of the automatic stabilization of normal operation (ASNOM) of such OCP (Fig. 1) [5].…”

“…According to previous studies carried out by the SI-method, the properties of dynamic objects in the electric power industry include the insufficient amount of information for making a decision, which appears due to the instability of the development of transient processes, the possibility of incomplete development of information components, passing through a variety of scenarios [15,16]. Insufficient amount of information Q is the cause of detection errors during recognition [5,15].…”

“…In previous works, a name was given to each elementary information component of TS, and NTS, semantic SN situations, and SceN scenarios [5], [9,10], [11,12], [14,15]. This will further develop the proposed mathematical SI-model GESOCP≈GESRPA for the hierarchical level of SceN scenario recognition.…”

“…The tasks of relay protection and automation (RPA) [5], diagnostics [6], self-control [7], economics [8] and others are reduced to the management of such a group of dynamic OCP. The tasks of recognising the semantic events SN, and SceN are characterized by irregularity, discontinuity of changes in time of development of transient processes [9,10].…”