Year: 2025
Type: article
Abstract: 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.
DOI: 10.4018/joeuc.369157
Authors: Jingbo Song, Yingli Wu, Duo Zhao, Jingqi Li, Liqi Ding
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4407435448