Doctoral Thesis Defense in Electrical Engineering by Mr. Adamou AMADOU

The Université Euromed de Fès (UEMF) is pleased to inform the public of the

defense of a doctoral thesis in Electrical Engineering

The thesis defense will take place on Wednesday, October 16, 2024, at 10:00 AM at UEMF

Location: Gallery of Building 1

The thesis will be presented by Mr. Adamou AMADOU

ADAMOU on the topic:

“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 enhancement. 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 represent 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 contribute 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 situated within the framework of Industry 4.0, where emerging technologies are redefining decision-making standards, a crucial aspect in industrial sectors. Among the major innovations, digital twins play an essential role in this industrial revolution, while artificial intelligence acts as a powerful catalyst for performance improvement. Energy efficiency in facilities is one of the fundamental pillars of Industry 4.0. To achieve the objectives of this new era, it is necessary either to build or replace existing industrial equipment to comply with these new standards, or to develop adaptive approaches to transform current systems into compliant ones suited to this revolution. To meet this need, our thesis proposes the creation of an energy efficiency-focused digital twin, specifically designed for induction motors already in service, which represent approximately 70% of the energy consumption of electrical machines in the industrial sector. This digital twin serves as an adaptive platform for these motors, thus illustrating the transformative potential of Industry 4.0. It predicts the functional efficiency and losses associated with the motors, while facilitating the detection of motor faults, making it a tool particularly suited for predictive maintenance, thereby contributing to energy savings. For example, an improvement of just 1% in the operational efficiency of induction motors could lead to savings of 105 TWh in the industrial sector. By implementing this solution, we aim to significantly improve energy efficiency and strengthen overall industrial performance.

Keywords:

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

This thesis will be presented before the jury members:

Last and First Name	Institution	Role
Pr. Abdelatif SAFOUANE	UEMF	President
Pr. Rachid EL BACHTIRI	EST-USMBA	Reviewer
Pr. Badre BOUSSOUFI	FS-USMBA	Reviewer
Pr. Ali HADDI	ENSA-UAE	Reviewer
Pr. Mohammed ELSALHI	ENSA-USMBA	Examiner
Pr. Rachid EL ALAMI	FS-USMBA	Examiner
Pr. Tijani BOUNAHMIDI	UEMF	Thesis Supervisor
Pr. Chakib ALAOUI	EPS-UEMF	Co-supervisor of the Thesis