Intelligent monitoring of industrial equipment:a study on fault prediction based on deep learning

Predictive maintenance is gaining increasing attention in the field of industrial equipment management as an effective strategy to enhance equipment reliability and reduce maintenance costs. Deep learning has become a focal point due to its exceptional ability to process time series data and recognize complex patterns. To address challenges related to accuracy and robustness in predicting equipment failures, this study proposes a novel model that combines deep reinforcement learning (DDPG) with gated recurrent units (GRU), alongside Bayesian Optimization for hyperparameter tuning. The DDPG component learns the dynamic interactions between actions and states, adapting to the specific characteristics of different devices. The GRU module is designed to capture temporal dependencies in sensor data.