IoT-Driven Predictive Maintenance in Industrial Settings through a Data Analytics Lens

Predictive maintenance in industrial settings has seen significant advancements through the integration of Internet of Things (IoT) technologies and data analytics. This study outlines a comprehensive methodology for implementing IoT-Driven Predictive Maintenance, focusing on data collection, machine learning model selection, real-time data analysis, and continuous improvement. Data Collection and Sensor Deployment details the deployment of sensors on machinery, including temperature, vibration, pressure, humidity, and accelerometer sensors. Real-time data collection, strategic sensor placement, and data preprocessing ensure the acquisition of high-quality data for analysis. Machine Learning Model Selection highlights the selection of Random Forest and LSTM models for predictive maintenance. These models are trained using historical data, cross-validated, and fine-tuned to optimize accuracy. Real-Time Data Analysis and Integration emphasizes the importance of real-time analysis using streaming analytics tools. Alerts are generated when sensor data deviates from predefined thresholds, and the integration with maintenance workflows ensures timely responses. Performance Evaluation and Continuous Improvement presents performance metrics, including precision, recall, and F1-score, demonstrating the effectiveness of the predictive maintenance system. Reduced downtime and decreased maintenance costs showcase its impact. Continuous improvement involves regular updates to models and data preprocessing techniques, with a focus on adapting to emerging technologies.