Harnessing CNN and LSTM Models for Enhanced Predictive Maintenance in Industrial IoT Systems

In this research, the problem of predictive maintenance using the Industrial Internet of Things (IIoT) systems is investigated with the use of hybrid Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) models. Depending on the temporal dependency in the sensor data we want to bring in the LSTM to capture the temporal relations in the data as well as to extract the spatial feature using the CNN. Using publicly available IIoT datasets, including NASA Prognostics Data Repository to train and evaluate the models. It results in accuracy of 94.5% vs traditional machine learning algorithms. Finally, edge computing was tested for real-time deployment, making these predictions within a second and maintenance interventions prompt. It is shown that the hybrid CNN-LSTM model drastically improves PdM accuracy over other models, thereby reducing downtime and maintenance costs. It is a promising solution for promoting predictive maintenance in industrial cases, based on the scalability and robustness of this approach. Finally, future work will understand data quality challenges and more advanced methods, such as reinforcement learning and digital twins, to further improve model performance.