Doctoral thesis defense in Electrical Engineering by Mr. Adamou AMADOU

The Euromed University of Fez (UEMF) is pleased to inform the public of the

doctoral thesis defense in Electrical Engineering

The thesis defense will take place on Wednesday October 16, 2024 at 10:00 a.m. at the UEMF

Location : the Gallery of Building 1

The thesis will be presented by Mr. Adamou AMADOU

ADAMOU Under the theme:

“Efficiency-Based Digital Twin Implementation For In-Services Induction Motors In Industry 4.0”

Abstract 

The thesis is situated within the context of Industry 4.0, where emerging technologies establish standards for decision-making, a critical aspect of industrial sectors. Key innovations, including Digital Twins, are vital to this industrial revolution, with artificial intelligence acting as a powerful catalyst for performance improvement. Energy efficiency in facilities is one of the foundational pillars of Industry 4.0. Achieving Industry 4.0 involves either constructing or replacing existing industrial equipment to meet these new standards or developing adaptive methods to transform current systems into compliant ones. To address this need, our thesis proposes the creation of an energy efficiency-based Digital Twin specifically designed for in-service Induction Motors, which represents approximately 70% of industrial electrical energy consumption among all electrical machines. This proposed Digital Twin serves as an adaptive platform for these motors, emphasizing the transformative potential of Industry 4.0. It predicts the efficiency and losses associated with the motors and enables fault detection, making it particularly suitable for predictive maintenance which contributes to energy saving. For instance, a mere 1% improvement in the operational efficiency of induction motors could result in saving 105 TWh in the industrial sector. By implementing this solution, we aim to significantly enhance energy efficiency and elevate overall industrial performance.

Keywords:

Induction Motors, Electrical equivalent circuit, Adaptive-Neuro-Fuzzy inference system, Energy efficiency, Losses, Digital twin, Digital Shadow, Industry 4.0.

Summary:

This thesis is part of Industry 4.0, where emerging technologies are redefining decision-making standards, which represents a crucial aspect in industrial sectors. Among the major innovations, digital twins play a vital role in this industrial revolution, while artificial intelligence acts as a powerful catalyst for improved performance. Energy efficiency in installations constitutes one of the fundamental pillars of Industry 4.0. To achieve the goals of this new era, it is necessary to either build or replace existing industrial equipment to comply with these new standards, or to develop adaptive approaches to transform current systems into compliant systems suited to this revolution. To meet this need, our thesis proposes the creation of a digital twin focused on energy efficiency, specially designed for already operational induction motors, which represent approximately 70% of the energy consumption of electrical machines in the sector. industrial. This digital twin serves as an adaptive platform for these engines, illustrating the transformative potential of Industry 4.0. It makes it possible to predict the functional efficiency and losses associated with motors, while facilitating the detection of motor faults, which makes it a tool particularly suited to predictive maintenance, thus contributing to energy savings. For example, an improvement of just 1% in the operational efficiency of induction motors could result in a saving of 105 TWh in the industrial sector. Through the implementation of this solution, we aim to significantly improve energy efficiency and strengthen the overall performance of the industry.

Keywords:

: Asynchronous motors, Equivalent electrical circuit, Adaptive neural fuzzy inference system, Energy efficiency, Losses, Digital twin, Digital shadow, Industry 4.0.

This thesis will be presented to the jury members:

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