Implementation of Reinforcement Learning in ManufacturingGoodson
Reinforcement learning is a subfield of artificial intelligence that focuses on enabling an agent to make decisions in an environment by continuously taking actions, observing the consequences, and learning from the rewards it receives for its actions. The ultimate goal of the agent is to learn a policy that maximizes its cumulative reward over time. In recent years, there has been growing interest in using reinforcement learning to address manufacturing problems.
We have recently applied RL in anomaly detection. Anomaly detection in manufacturing involves identifying any unusual or unexpected behavior in a production system, which can indicate potential problems such as equipment malfunctions, process disruptions, or quality issues. The goal of anomaly detection is to identify these deviations as early as possible so that corrective actions can be taken to minimize the impact on production and prevent costly downtime.
In RL-based anomaly detection, the agent acts as a detector and learns to identify anomalies in the factory process data through trial-and-error. The agent’s performance is evaluated based on its ability to accurately identify anomalies and receive rewards for successful detections. The agent’s policy for detecting anomalies is constantly updated and improved as it interacts with the data, making it capable of detecting anomalies in different types of devices within the factory. Thus, the agent serves as a single anomaly detector for the entire factory.
The reward equation in RL-based anomaly detection incentivizes the agent to predict anomalies as early as possible while minimizing false positive detections. The health of the factory process is indicated by the range of predicted anomaly scores, allowing the operator to quickly identify any deviations from the normal process. The root cause of these deviations is also displayed, providing the operator with crucial information to take the necessary corrective actions.