Reinforcement Learning-Based Real-Time Contrasts Control Chart Using an Adaptive Window Size

In this study, we propose a real-time contrasts control chart based on reinforcement learning (RL-RTC). Effective process monitoring, which directly influences productivity and yield, has become increasingly important. The traditional RTC control chart offers a promising approach to process monitoring by transforming the monitoring problem into a real-time classification task, enabling more timely and accurate detection of issues. However, the parameters of the traditional RTC control chart are typically set based on empirical methods, which limits the ability to fine-tune them effectively. To address this limitation and improve the performance of the RTC method, we propose the RL-RTC control chart, which leverages reinforcement learning for more adaptive parameter control. The proposed method takes the data itself as a state of reinforcement learning (RL) and adaptively decides the parameter of the RTC control chart in real-time. In this paper, the control of the moving window size is defined as an action taken by the RL agent. By automatically learning an optimal policy, the RL-RTC method eliminates the need for manual parameter tuning and enhances adaptability in dynamic process environments. The RL-RTC approach can detect shifts more quickly while maintaining the ability to identify the causes of faults. Compared to a conventional RTC control chart, experimental results demonstrate that the RL-RTC method offers improved performance by fine-tuning the window size in response to changing process conditions. Therefore, a wide scope of applications is expected for adaptively controlling the other RTC control chart parameters.