An Adaptive Sampling Strategy for Online Monitoring and Diagnosis of High-Dimensional Streaming Data

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.

Keywords:

• Adaptive sampling
• High-dimensional streaming data
• Online monitoring and diagnosis
• Tensor recovery

Supplementary Materials

Technical details: Document containing the proofs of Proposition 1 (Section 4.2), Proposition 2 (Section 5), and Proposition 3 (Section 5), and the steps to estimate the control limit h given a prescribed in-control ARL (Section 6). (.pdf file).

Additional figures and videos: Folder containing (i) a document with additional figures for the simulations (Section 7), (ii) a video of one simulation run and the corresponding samples selected by TSS, estimated sparse component, and control chart (Section 7), and (iii) the streaming tensors of the case studies and the corresponding samples selected by TSS, estimated sparse components, and control charts (Section 8). (.zip file).

MATLAB codes: Folder containing the codes to generate the data described in the simulations (Section 7) and to perform the TSS method described in this article. (.zip file).

Division of Civil, Mechanical and Manufacturing Innovation;

Acknowledgments

The authors thank to the editor, associate editor, and two anonymous referees for their constructive comments and suggestions that have considerably improved the article.

Additional information

Funding

The work of Paynabar was partially supported by the National Science Foundation (Grant no. CMMI-1839591).