Smart Hybrid Approach for Defect Detection Based on Analysis of System Entropy (DDetect)

Project no.: PP-91M/19

Project description:

Scientists and researchers from the Centre for Nonlinear Systems have experience in investigating models of real-world systems. Real data which was obtained experimentally and describes physical and physiological processes will be investigated during this project. Detection of non-typical or defect related measurements is one of the most important problems in engineering, medicine and other areas. In the first scenario the existence of a fault is identified and further degradation of the system is prevented while in the second – the illness is diagnosed. Constant increase in amounts of diagnostic and measurement data implies the need of automated and smart solutions for the detection of defects. Novel aspect of this project is the joining of artificial intelligence and methods of analysis of nonlinear dynamical systems. The data will be analyzed not directly but by performing computations, during which representative two-dimensional digital images based on permutation entropy, Wada characteristics, time averaged geometric moiré will be constructed, first. Thus the problem of detection of defects in signals is transformed to the problem of image classification and/or identification. This problem will be dealt with methods of artificial intelligence during this project. Additionally, smart approach for detection of defects without prior knowledge of their existence will be developed.

Project funding:

KTU R&D&I Fund

Period of project implementation: 2019-04-01 - 2019-12-31

Project coordinator: Kaunas University of Technology

Mantas Landauskas

2019 - 2019

Department of Mathematical Modelling, Faculty of Mathematics and Natural Sciences

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