RLAD Case StudyGoodson
One of the biggest challenges is the rarity and unpredictability of anomalies and failures, resulting in insufficient data to build a complete and accurate failure prediction model in real-world scenarios. In addition, the need for multiple models to address the different types of devices in a single plant can make operations and maintenance challenging. This is because these multiple models operate under different assumptions about the underlying patterns in each device’s data set.
To address these challenges, our team has developed an RL-based solution with the following features:
- The RL-based solution is designed to learn more effective anomaly detection strategies with a feasible amount of data.
- The solution employs a single anomaly detection model for monitoring the health of the entire factory, reducing the complexity of operations and maintenance.
The RLAD agent uses plant state as observations to predict anomaly scores for early detection of potential failures. The reward system is designed to avoid false detections and the agent’s predictions are used to indicate the plant’s health and root cause of abnormality. This helps operators schedule maintenance and minimize impact on production efficiency. The RLAD can also be applied to quality control and process monitoring in manufacturing.
Thus, reinforcement learning is a promising approach for solving manufacturing problems. Its ability to handle complex, dynamic systems and make decisions based on uncertain and changing information makes it well-suited for applications in production scheduling, predictive maintenance, and inventory control.