The most recent advancements in the Industrial Internet of Things (IIoT) technologies, which enable industrial equipment data monitoring and collection in real-time, have allowed for the implementation of predictive maintenance techniques that enhance operational efficiency and reduce unscheduled downtimes. This research analyzes multiple models of machine learning (ML) including Random Forests, Support Vector Machine (SVM), XGBoost, and Long Short-Term Memory (LSTM) networks with the goal of optimizing fault detection and performance evaluation in the industry. Sensor data from critical machinery was processed to assess model precision, recall, F1-score, and overall degradation forecasting to measure detection accuracy. Findings demonstrate that while XGBoost performs reliably for fault classification, early anomaly detection is best facilitated by LSTM networks due to their ability to capture relevant underlying temporal structures associated with such detection. The results showcased the importance of tailored machine learning model selection towards specific industrial use cases and the role of intelligent analytics aimed at enhancing predictive maintenance integration within IIoT frameworks.
 

Predictive Maintenance, Industrial Internet of Things (IIoT), Machine Learning Models, Fault Detection, Performance Optimization, Anomaly Detection