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Journal article
On wavelet-based statistical process monitoring
Transactions of the Institute of Measurement and Control, Vol.44(3), pp.525-538
2020
Web of Science ID: WOS:000549982700001
Abstract
This paper presents an overview of wavelet-based techniques for statistical process monitoring. The use of wavelet has already had an effective contribution to many applications. The increase of data availability has led to the use of wavelet analysis as a tool to reduce, denoise, and process the data before using statistical models for monitoring. The most recent review paper on wavelet-based methods for process monitoring had the goal to review the findings up to 2004. In this paper, we provide a recent reference for researchers and engineers with a different focus. We focus on: (i) wavelet statistical properties, (ii) control charts based on wavelet coefficients, and (iii) wavelet-based process monitoring methods within a machine learning framework. It is clear from the literature that wavelets are widely used with multivariate methods compared to univariate methods. We also found some potential research areas regarding the use of wavelet in image process monitoring and designing control charts based on wavelet statistics, and listed them in the paper.
Details
- Title
- On wavelet-based statistical process monitoring
- Publication Details
- Transactions of the Institute of Measurement and Control, Vol.44(3), pp.525-538
- Resource Type
- Journal article
- Publisher
- Sage Publications Ltd.; United Kingdom
- Copyright
- The Author(s) 2020
- Identifiers
- WOS:000549982700001; 99380090785206600
- Academic Unit
- Mathematics and Statistics; Hal Marcus College of Science and Engineering
- Language
- English