Abstract

Statistical process control techniques have been widely used for online process monitoring and diagnosis of streaming data in various applications, including manufacturing, healthcare, and environmental engineering. In some applications, the sensing system that collects online data can only provide partial information from the process due to resource constraints. In such cases, an adaptive sampling strategy is needed to decide where to collect data while maximizing the change detection capability. This article proposes an adaptive sampling strategy for online monitoring and diagnosis with partially observed data. The proposed methodology integrates two novel ideas (i) the recursive projection of the high-dimensional streaming data onto a low-dimensional subspace to capture the spatio-temporal structure of the data while performing missing data imputation; and (ii) the development of an adaptive sampling scheme, balancing exploration and exploitation, to decide where to collect data at each acquisition time. Through simulations and two case studies, the proposed framework’s performance is evaluated and compared with benchmark methods.

摘要

统计过程控制技术已广泛用于在线过程监控和诊断各种应用中的流数据，包括制造、医疗保健和环境工程。在某些应用中，由于资源限制，收集在线数据的传感系统只能提供来自过程的部分信息。在这种情况下，需要一种自适应采样策略来决定在哪里收集数据，同时最大限度地提高变化检测能力。本文提出了一种自适应采样策略，用于基于部分观测数据的在线监测和诊断。所提出的方法集成了两个新颖的想法（i）将高维流数据递归投影到低维子空间上，以在执行缺失数据插补的同时捕获数据的时空结构；(ii) 开发自适应采样方案，平衡探索和开发，以决定在每个采集时间收集数据的位置。通过模拟和两个案例研究，评估了所提出框架的性能并与基准方法进行了比较。