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Titre: | Electrical systems faults diagnosis based on thermography and machine learning techniques |
Auteur(s): | Mahami, Amine Benazzouz, Djamel(Directeur de thèse) |
Mots-clés: | Induction motor Infrared thermography images Electrical transforme Faults diagnosis Extremely randomized tree Machine learning |
Date de publication: | 2025 |
Editeur: | Université M'Hamed Bougara Boumerdès : Faculté de Technologie |
Résumé: | The goal of using AI-driven conditional monitoring in electrical devices is to
monitor and trace the beginning and development of deterioration prior to a failure.
This degradation eventually results in a system malfunction that impacts the availability of the
whole system. Early identification allows for a planned shutdown, averting catastrophic
failure and guaranteeing more cost-effective and dependable operation.
This study is divided into two major parts: the first part deals with the
identification and categorization of faults in induction motors, and the second part deals with the
detection and classification of faults in transformers.
In machine health management, condition monitoring and problem diagnostics
of electrical machines are important study areas. Using infrared thermography method
(IRT), a new noncontact and nonintrusive experimental framework is used in the first portion
of this thesis to monitor and diagnose defects in a three-phase induction motor. Using
IRT to obtain a thermograph of the target machine is the first step in the process. The
Speeded-Up Robust Features (SURF) detector and descriptor are then used to extract fault
features from the IRT images using the bag-of-visual-word (BoVW) technique. Then, a
group learning method known as Extremely Randomized Tree (ERT) is applied to
automatically detect different types of induction motor defect patterns. Based on
experimental IRT images, the efficacy of the suggested method is evaluated, showcasing its potential as a potent diagnostic tool with superior classification accuracy and stability over
alternative approaches.
The second part of the thesis presents an experimental framework that uses
infrared thermography (IRT) to monitor and diagnose transformer defects in a non-intrusive
and non-contact manner. Using IRT to obtain a thermograph of the intended machine is the first
step in the process. GIST features are then taken from the database's reference image and
every other image. Finally, a machine learning technique known as Support Vector Machine
(SVM) is used to automatically identify different fault patterns in the transformer. Based on
experimental IRT images and diagnostic results, the efficacy and capacity of the proposed method are assessed,
demonstrating its potential as a potent diagnostic tool with high classification accuracy and
stability. This method improves operational reliability by facilitating the early identification and
detection of transformer failures |
Description: | 79 p. : ill. ; 30 cm |
URI/URL: | http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/15431 |
Collection(s) : | Doctorat
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