Transforming Industrial Supervision Systems: A Comprehensive Approach Integrating Machine Learning Techniques and Fuzzy Logic

• Published in: The scientific bulletin of Electrical Engineering Faculty Vol. 24; no. 2; pp. 52 - 66
• Main Authors: Zermane, Hanane, Ziar, Ahcene, Madjour, Hassina, Touahar, Djamel
• Format: Journal Article
• Language: English
• Published: Targoviste Sciendo 01.12.2024; De Gruyter Poland
• Subjects: Accuracy; Cement Industry; Classification; Fuzzy Control; Fuzzy logic; Industrial Optimization; Intelligent Systems; Knowledge Exploitation; Machine Learning; Predictive Analysis; Process Efficiency; Real-Time Control; Regression analysis; Supervision; Support Vector Machine
• ISSN: 2286-2455; 1843-6188; 2286-2455
• DOI: 10.2478/sbeef-2024-0021

In addressing the mounting challenges of industrial supervision systems grappling with intricate processes, this study pioneers a transformative paradigm centered on the SCIMAT cement factory. By seamlessly integrating Machine Learning and Fuzzy Logic, the primary aim is to revolutionize real-time control systems, with a keen focus on cement production. SVM integration into the supervision system, coupled with connectivity to a Programmable Logic Controller (PLC), is complemented by fuzzy real-time controllers’ regression analysis. Rigorous testing and evaluation validate the proposed approach’s reliability, showcasing its effectiveness in discerning optimal system functioning. The system’s practical application within a PLC environment underscores its prowess in issuing commands to industrial equipment, thereby enhancing operational efficiency. Going beyond conventional methodologies, our approach amalgamates SVM classification, fuzzy controllers, and real-time regression analysis, delivering a multifaceted solution for industrial supervision. The system’s standout achievement is an SVM classification accuracy surpassing 94% compared to other classifiers. The K-Nearest Neighbors (K-NN) model demonstrated an accuracy rate of approximately 93.83%. The decision tree model attained an accuracy of around 83.73%. The logistic regression model achieved an accuracy of 80.25%. These models are not only adept at distinguishing optimal functioning from faults but also adept at preserving the linguistic language used by operators. The study’s novelty lies in the holistic integration of SVM and Fuzzy Logic, offering a practical and adaptable solution that not only advances classification accuracy but also significantly reduces maintenance costs, marking a substantial improvement over the traditional methods. This transformative model, validated through SVM classification and practical application, establishes a new standard for flexibility, cost reduction, and overall productivity enhancement in industrial processes.