Adaptive Sampling for Monitoring Multi-Profile Data with Within-and-between Profile Correlation

Jinwei Yao; Xiaochen Xian; Chao Wang

doi:10.1080/00401706.2023.2166125

Back

Journal article

Open access

Peer reviewed

Adaptive Sampling for Monitoring Multi-Profile Data with Within-and-between Profile Correlation

Jinwei Yao, Xiaochen Xian and Chao Wang

Technometrics, Vol.65(3), pp.375-387

2023

DOI: 10.1080/00401706.2023.2166125

Files and links (1)

url

https://figshare.com/articles/dataset/Adaptive_Sampling_for_Monitoring_Multi-Profile_Data_with_Within-and-between_Profile_Correlation/22047916View

Open Access

Abstract

Multi-profile data can provide within-and-between profile information for efficiently modeling and monitoring system status. In practice, however, acquisition of such data requires large number of sensors, which raises various concerns and difficulties, for example, cost, energy, and data transmission bandwidth, in accessing the full data from each sensor. In this article, we propose an adaptive sampling strategy for multi-profile monitoring by using limited portion of data. The proposed sampling and monitoring scheme incorporates the within-and-between profile correlation and features the balance between random search and greedy search in identifying the most informative profiles. More specifically, the multivariate functional principal component analysis (MFPCA) is used to capture the within-and-between profile correlation, and the MFPC scores are augmented for unobservable profiles to feed into a multivariate CUSUM chart. Two properties of the proposed method for allocating sampling resources among sensors are investigated. Numerical and case studies are conducted under various scenarios to demonstrate the effectiveness of the method.

Adaptive sampling

Manufacturing systems

MCUSUM

MFPCA

Profile monitoring

Details

Title: Subtitle: Adaptive Sampling for Monitoring Multi-Profile Data with Within-and-between Profile Correlation
Creators: Jinwei Yao - University of Iowa
Xiaochen Xian - University of Florida
Chao Wang - University of Iowa
Resource Type: Journal article
Publication Details: Technometrics, Vol.65(3), pp.375-387
DOI: 10.1080/00401706.2023.2166125
ISSN: 0040-1706
eISSN: 1537-2723
Publisher: Taylor & Francis
Grant note: name: Engineering Research and Development Center; DOI: 10.13039/100000104, name: National Aeronautics and Space Administration
Language: English
Electronic publication date: 02/06/2023
Date published: 2023
Academic Unit: Industrial and Systems Engineering
Record Identifier: 9984366358702771

Metrics

77 Record Views

9 Times Cited - Web of Science