Performance Evaluation of RNN Models for Predictive Maintenance in IOT Systems

This paper evaluates the performance of various RNN models, including Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, in predicting equipment failures and maintenance needs. Through extensive experiments and comparative analysis, we assess the accuracy, computational efficiency, and scalability of these models in real-world IoT scenarios. The results demonstrate that RNN-based models can significantly enhance predictive maintenance strategies, leading to improved operational efficiency and reduced maintenance costs. Key challenges, such as data preprocessing, model selection, and implementation issues, are also discussed, providing a comprehensive overview for researchers and practitioners in the field.