Deep Learning based Computational Intelligence for Real-Time Predictive Maintenance in Industrial IoT Systems

Abstract

The study examines the use of deep learning for predictive maintenance in real-time in the Industrial Internet of Things (IIoT) systems with the aim of improving the accuracy of the prediction of failures through affordable methods of computational intelligence. With the growing dependence of the industries on automated systems, it is critical to ensure the reliability of equipment to maintain the continuity of the processes and decrease the instances of unexpected failures. The conventional methods of maintenance that are usually based on a planned maintenance or a reactive strategy are not efficient and accurate enough to suit the new industrial environment. This paper introduces a deep learning-based architecture, which incorporates neural networks and time-series analysis to enhance the forecasting of equipment breakdowns called DeepTimeNet. Through real-time sensor data of industrial equipment, the model can track equipment health continuously to provide real-time updates and predictions of failures. The most important are significant performance improvements, as the proposed model DeepTimeNet demonstrates 95.50% of accuracy, 93.50% of precision, and 93.85% of F1-score in failure prediction. The findings demonstrate the ability of the model to predict possible failures with high precision and efficiency, and this is much better than conventional machine learning algorithms such as Support Vector Machines (SVM), Random Forest, and Logistic Regression. The paper ends with the identification of the influence of the model in enhancing cost-effectiveness and reliability in IIoT systems and offers future research directions, such as adding more sensors of the IoT, transfer learning methods, and hybrid models to further improve the prediction accuracy of complex industrial systems.