BELAYALI RezkiaOURARI Tinhinane2024-12-102024-12-102024https://dspace.estin.dz/handle/123456789/20PdM has significantly evolved within Industry 4.0, leveraging ML models to enhance system health assessment and failure prediction. Single models such as ANNs and SVM have demonstrated effectiveness in specific tasks but often struggle with complex datasets and the intricate nature of real-world systems. For instance, ANNs used for tram track gauge deviation prediction show high accuracy but require the incorporation of additional factors such as temperature and load conditions to handle complex datasets effectively. Similarly, SVMs are proficient in distinguishing between normal and abnormal states but face scalability and computational efficiency challenges with large, heterogeneous datasets. However, single models can still be highly effective with the right input data. By focusing on feature engineering and selection to improve data quality and incorporating domain knowledge, such as industryspecific laws and principles, we can extract meaningful features and patterns, enabling single models to better handle complex datasets and leverage their strengths. Multi-model and hybrid approaches have emerged as more comprehensive solutions, offering improved accuracy and reliability, particularly when combining physics-based models with statistical techniques and ML algorithms. These approaches are especially effective in capturing complex patterns and trends within data, improving the robustness and accuracy of PdM systems. For example, physics-based models provide a deep understanding of system behavior and underlying failure mechanisms. When combined with statistical models, they facilitate precise feature extraction, enhancing the quality of data fed into ML algorithms. Multi-model frameworks can detect anomalies more effectively by leveraging the strengths of different models. For instance, clustering algorithms can identify normal operating states, while supervised ML models can classify detected anomalies. Hybrid approaches using DL models like Convolutional Neural Networks and LSTM networks can predict RUL with high accuracy, benefiting from the rich feature sets derived from physics-based models. Future researchers should explore further integration of different approaches and models to leverage their strengths and build a more robust framework. Hybrid approaches that combine DL with physics-based knowledge show particular promise in improving fault detection and RUL estimation. The findings from this comparative study will set the stage for the practical component of our research, which will be outlined in a separate report. This practical phase will delve deeper into the implementation of a specific PdM approach within an industrial setting, leveraging the insights gained from the literature review and objective analysis. This integration of theoretical insights with practical application will provide a comprehensive understanding of PdM strategies and efficacy.This study explores the evolving role of machine learning in revolutionizing predictive maintenance (PdM) within Industry 4.0, emphasizing the transition from traditional methods to advanced, data-driven approaches, particularly highlighting deep learning's transformative impact. It examines key technologies such as IoT (Internet of Things) sensors for real-time vibration analysis and addresses the efficacy of data-driven models, stressing the importance of managing data quality. The study also explores state-of-the-art approaches that integrate both single-model and multi-model frameworks, combining machine learning (ML) with physics-based models and statistical techniques. This integrated approach enhances anomaly detection, fault classification, and estimation of remaining useful life (RUL), contributing to a robust PdM framework designed for Industry 4.0 environments. Keywords: Predictive maintenance, industry 4.0, machine learning, single model, multimodel, data-driven, IoT, vibration, RUL, anomaly detection, classification.enThesis