DSpace Collection:http://dspace.ensta.edu.dz/jspui/handle/123456789/1732026-03-24T14:36:59Z2026-03-24T14:36:59ZImplementing Machine Learn-ing for Industrial Electrical Systems Maintenance and Re-mote MonitoringKHELFAOUI, Rabah AbdennourTEDJINII, OmarMERADI, Samir (Directeur de thèse)http://dspace.ensta.edu.dz/jspui/handle/123456789/3852025-11-03T12:03:35Z2025-01-01T00:00:00ZTitle: Implementing Machine Learn-ing for Industrial Electrical Systems Maintenance and Re-mote Monitoring
Authors: KHELFAOUI, Rabah Abdennour; TEDJINII, Omar; MERADI, Samir (Directeur de thèse)
Abstract: Predictive maintenance and remote monitoring have become essential
components in modern industrial environments, particularly for
electrical systems, which are critical to ensuring operational continuity,
safety, and productivity. Tra-ditional maintenance strategies, based on
reactive or time-based interventions, of ten lead to unexpected failures,
increased costs, and inefficient use of resources. This research addresses
these limitations by proposing an intelligent maintenance system that
integrates Machine Learning (ML) and Internet of Things (IoT) tech-
nologies to predict failures and enable real-time monitoring of industrial
electrical equipment. The methodology involves collecting real-time data
from electrical systems using smart sensors, transmitting it through an
loT infrastructure, and applying ML algorithms to analyze equipment
behavior, detect anomalies, and forecast potential failures. The system is tested under simulated industrial con-ditions to assess its accuracy,
responsiveness, and usability. A case study on a DC motor equipped with
sensors (temperature, humidity, current, and vibration) and connected to
an ESP8266 microcontroller demonstrates the system's effective ness,
with data transmitted via MQTT to a cloud platform and processed using
the Random Forest algorithm, achieving 90% accuracy in fault
classification. The results show significant improvements in fault
detection, maintenance scheduling. and system reliability, contributing to
the development of intelligent maintenance frameworks and supporting
the digital transformation of industrial practices in alignment with
Industry 4.0 objectives.
Description: Master : Programme Complémentaire d'Ingéniorat: Management et ingénierie de la maintenance industrielle : Alger: Ecole Nationale Supérieure des Technologie Avancées: 20252025-01-01T00:00:00ZMachine Learning Driven Predic-tive Maintenance for Industrial IoT of Electrica SystemsAbderraouf, ChiabaMeradi, Samir (Directeur de thèse)http://dspace.ensta.edu.dz/jspui/handle/123456789/3842025-11-03T11:59:16Z2025-01-01T00:00:00ZTitle: Machine Learning Driven Predic-tive Maintenance for Industrial IoT of Electrica Systems
Authors: Abderraouf, Chiaba; Meradi, Samir (Directeur de thèse)
Abstract: This study introduces a predictive maintenance framework that
combines Internet of Things (IoT) sensor data with advanced machine
learning (ML) techniques to improve the reliability and efficiency of
industrial electrical systems. The proposed system analyzes real-time
measurements such as temperature, vi bration, current, and voltage to
detect anomalies and predict equipment failures before they occur. It
integrates essential processes including data cleaning, nor-malization,
feature extraction, and model optimization to ensure accurate and timely
predictions. Several ML models, including Random Forest, Support
Vector Machines (SVM), and Artificial Neural Networks (ANNs), were
evaluated for their performance. A real-world case study involving three
industrial motors was con ducted using over 10,000 time-series sensor
records, 500 maintenance logs, and 100 documented failure cases. The results demonstrate a 35% reduction in unplanned downtime, a 25%
increase in resource efficiency, and a 20% extension of equipment
lifespan. These findings validate the effectiveness of the proposed
approach in sup porting proactive maintenance decisions in industrial
environments.
Description: Master : Programme Complémentaire d'Ingéniorat: Management et ingénierie de la maintenance industrielle : Alger: Ecole Nationale Supérieure des Technologie Avancées: 20252025-01-01T00:00:00ZIntegration of CMMS in Industry 4.0ZERROUK, Ikram Feth EzahrGUENDOUZI, Meriem LydiaREZGUI, Wail (Directeur de thèse)http://dspace.ensta.edu.dz/jspui/handle/123456789/3832025-11-03T11:55:46Z2025-01-01T00:00:00ZTitle: Integration of CMMS in Industry 4.0
Authors: ZERROUK, Ikram Feth Ezahr; GUENDOUZI, Meriem Lydia; REZGUI, Wail (Directeur de thèse)
Abstract: The emergence of Industry 4.0 has significantly reshaped industrial maintenance by promoting the integration of intelligent technologies that enhance efficiency, reliability, and data-driven decision-making.
Traditional Computerized Maintenance Management Systems (CMMS), once focused solely on planning and documentation, are now evolving into smart platforms capable of supporting real-time monitoring, predictive analytics, and automated maintenance workflows. This paper explores the integration of Industry 4.0 technologies such as IoT, Artificial Intelligence, and Augmented Reality within CMMS environments. It highlights commonly adopted solutions, identifies key limitations and discusses futures opportunities such as integrating emerging technologies like digital twins and advanced AI tools, including Natural Language Processing (NLP) and Large Language Models (LLM). This work aims to provide insights into building next-generation CMMS aligned with the principles of Industry 4.0.
Description: Master : Programme Complémentaire d'Ingéniorat: Management et ingénierie de la maintenance industrielle : Alger: Ecole Nationale Supérieure des Technologie Avancées: 20252025-01-01T00:00:00ZProcedures et techniques d’amélioration de la fonction requise des systèmes industrielsAit Djida, RoumaissaElmaouhab, HadjerSalhi, Nedjma (Directeur de thèse)http://dspace.ensta.edu.dz/jspui/handle/123456789/3822025-11-03T11:49:37Z2025-01-01T00:00:00ZTitle: Procedures et techniques d’amélioration de la fonction requise des systèmes industriels
Authors: Ait Djida, Roumaissa; Elmaouhab, Hadjer; Salhi, Nedjma (Directeur de thèse)
Abstract: Dans l’environnement industriel actuel, l’amélioration de la fonction requise
des systèmes est considérée comme un axe important et crucial, mais
également comme un enjeu complexe. Cette complexité est accentuée par les
exigences croissantes en matière de fiabilité, de rentabilité, de durabilité et de
compétitivité sur le marché, tous secteurs confondus.
Pour relever ce défi, de nombreuses techniques et méthodes, allant des
approches traditionnelles aux plus avancées, ont été développées, notamment
avec l’émergence de l’industrie 4.0.
Les recherches récentes ont identifié plusieurs procédés visant à optimiser et à
améliorer la fonction requise des systèmes industriels. Il s’agit notamment de
techniques basées sur l’intelligence artificielle pour le diagnostic et le
pronostic, utilisant le Machine Learning, le Deep Learning et les réseaux de
neurones, soutenues par l’Internet des Objets (IoT). À cela s’ajoutent des
méthodes avancées de maintenance prédictive et de surveillance, ainsi que des
approches Lean telles que le SMED (Single-Minute Exchange of Die) et la
TPM (Total Productive Maintenance), qui ont été mises en œuvre pour
améliorer les performances des systèmes industriels.
L’objectif principal de cet article est de synthétiser les avancées récentes en
matière de procédures et de techniques de maintenance utilisées pour
renforcer la fonction requise des systèmes industriels. Il met en lumière les
contributions et les applications de ces différentes méthodes.
Description: Master : Programme Complémentaire d'Ingéniorat: Management et ingénierie de la maintenance industrielle : Alger: Ecole Nationale Supérieure des Technologie Avancées: 20252025-01-01T00:00:00Z