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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.
Authors:
Keywords
nonlinear dynamic objects; diagnostic models; model reduction; spectral transformations
DOI
10.15276/aait.01.2020.5
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Received 28.01.2020
Received after revision 17.02.2020
Accepted 19.02.2020
Published:
Vol. 3 № 1, 2020
Last download:
17 May 2022

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Contents

Information Systems and Technologies

1. Models and Methods of Information Technology2. Design of Information Systems and Technologies3. Mathematical Issues of Information Technologies4. Innovative Technologies in Education, Culture and art5. Game Technologies, Augmented and Virtual Reality6. Theoretical and Applied Issues of Computer Science7. Project, Program and Portfolio Management

Digital control of Technical and Social Systems

1. Adaptive and optimal Control Systems2. Parametric and System Identification3. Interconnected Systems and Systems with Distributed Parameter4. Renewable Energy Systems5. Machine Learning and Artificial Intelligence in General Technical Problems and Electromechanics6. Management of Production and Power Plants7. Control Systems for Robotic Systems and Complexes, Electric Vehicles8. Diagnosis and Evaluation of Complex Systems9. Simulation of Physical Objects and ProcessesSensor less Control Systems

Software Engineering and Systems Analysis

1. Methods and Means of Intellectual Information Processing2. Recognition, Decision Making, Forecasting3. Neural Network Technologies and Machine Learning Methods4. Semantic Models. Natural Language Processing5. Theoretical and Applied Issues of Software Engineering6. Models and Methods of Software Quality Management

Computer Systems and Cybersecurity

1. Parallel and Distributed Information Processing2. Signal Processing In Robotic Systems3. Internet of Things4. Information Security and Cybersecurity5. Computer Networks and Systems6. Components of Robotic Systems
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