Cost-effective dynamic sampling in high dimensional online monitoring with advantage actor-critic

Abstract

Industry 5.0 puts a spotlight on the team effort between people and machines, making the fast-paced development of IoT technology and sensor systems crucial. However, limitations such as the high cost of data acquisition, constrained bandwidth, and computational capacity can often hinder the real-time processing needed for human machine collaboration. To address this issue, we propose an integrated framework using Advantage Actor-Critic (A2C) and statistical process control (SPC) for economically monitoring high-dimensional data streams online. Our method leverages deep reinforcement learning with SPC charts to quickly identify system issues by smartly watching a few essential data streams. This approach not only saves resources but also boosts both the sustainability and efficiency of monitoring systems. Specifically, we construct a nonparametric monitoring statistic for each stream and develop a reinforcement learning framework to automatically identify the most informative and minimal number of data streams for observation at each time epoch. Our strategy is proven effective through simulations and a practical example in smart manufacturing, showcasing its superiority in reducing detection delay and minimising resources use compared to state-of-the-art algorithms.

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