IIoT EL Expt Data

This paper focuses on advancements in predictive maintenance driven by artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT). It explores applications in the predictive maintenance in industries, aiming to provide a comprehensive understanding of current methodologies and future prospects. The discussion focuses on predictive maintenance methodologies, highlighting strengths, limitations, challenges, and opportunities. It also evaluates machine learning libraries and traditional algorithms, emphasizing the importance of choosing frameworks based on project requirements and efficiency considerations. Additionally, a comparison of algorithms, including K-Neighbors, Support Vector Machine, and Random Forest, is conducted, with the Random Forest model being chosen for further analysis. The provided Python codes demonstrate data analysis and prediction using a RandomForestClassifier model, giving understanding of feature importance and dataset characteristics. The findings have implications for various real-world applications, suggesting avenues for further research, such as advanced feature engineering, hyperparameter tuning, ensemble learning, and time-series analysis techniques to enhance predictive modeling performance.