A Neural Network-based Adaptive Sampling in Monitoring High-dimensional Processes

Multivariate statistical process control techniques have been widely used for online monitoring in various applications. However, when monitoring high-dimensional processes, practical resource constraints such as limited data transmission bandwidth, number of sensors, and sensor battery life often restrict our ability to collect observations from all data streams. In this paper, we propose a neural network-based adaptive sampling strategy for monitoring high-dimensional processes. To adaptively select data streams for observation, it is essential to dynamically assess the importance of each stream. Accordingly, we propose learning an importance function through a neural network. By incorporating domain knowledge of process monitoring, a monotonic neural network is constructed. A key challenge in training this network is the lack of ground truth for importance assessment. To address this challenge, we simulate training data and design a novel loss function. Compared to existing adaptive sampling strategies, our method does not rely on heuristic techniques and offers enhanced scalability. With the proposed adaptive sampling strategy, a generic monitoring scheme is then developed for the monitoring of high-dimensional processes. We have conducted thorough numerical simulations and a case study, which demonstrate that our method significantly reduces detection delays and increases the likelihood of observing shifted data streams.