Enhancing Predictive Maintenance in Industry 4.0: Leveraging AI for Optimal Machinery Health Management

This research introduces a multi-stage predictive maintenance framework to enhance the prediction of Remaining Useful Life (RUL) in machinery by leveraging advanced AI-driven models. The framework comprises two primary phases: development modeling and active prediction. In the development phase, Conditional Generative Adversarial Networks (CGAN) are employed to synthesize fault data and address class imbalances during data preparation and training. A hybrid model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) layers processes time-series data, effectively capturing both local temporal features and long-term dependencies for accurate RUL prediction. The active prediction phase utilizes real-time data to continuously monitor machinery health and provide timely RUL forecasts. The system integrates a Fault-Finding Reference Unit and an Error Condition Classification Unit, where fault types are classified using Gated Recurrent Units (GRU) enhanced through active continual learning. Human experts contribute by updating the knowledge base, ensuring the system adapts and improves over time. This comprehensive approach aims to optimize maintenance schedules, reduce machine downtime, and enhance operational efficiency in Industry 4.0 environments.