Publication Type
Journal Article
Version
acceptedVersion
Publication Date
1-2021
Abstract
Tube internal erosion, which corresponds to its wall thinning process, is one of the major safety concerns for tubes. Many sensing technologies have been developed to detect a tube wall thinning process. Among them, fiber Bragg grating (FBG) sensors are the most popular ones due to their precise measurement properties. Most of the current works focus on how to design different types of FBG sensors according to certain physical laws and only test their sensors in controlled laboratory conditions. However, in practice, an industrial system usually suffers from harsh and dynamic environmental conditions, and FBG signals are affected by many unpredictable factors. Consequently, the FBG signals have more fluctuations and are polluted by noises. Hence, the signals no longer directly follow the assumed physical laws and their proposed thinning detection mechanisms no longer work. Targeting at this, this article develops a data-driven model for FBG signal feature extraction and tube wall thickness monitoring using data analytic techniques. In particular, we develop a spatiotemporal model to describe dynamic FBG signals and extract features related to thickness. By taking physical law as guideline, we trace the relationship between the extracted features and the tube wall thickness, based on which we construct an online statistical monitoring scheme for tube wall thinning process. We use both laboratory test and field trial experiment to demonstrate the efficacy and efficiency of the proposed scheme.
Keywords
Electron tubes, Sensors, Monitoring, Fiber gratings, Feature extraction, Strain, Corrosion, Fiber Bragg grating (FBG) sensors, online monitoring, spatiotemporal model, statistical process control, tube erosion detection
Discipline
Numerical Analysis and Scientific Computing | Operations Research, Systems Engineering and Industrial Engineering
Research Areas
Data Science and Engineering
Publication
IEEE Transactions on Automation Science and Engineering
Volume
19
Issue
1
First Page
441
Last Page
456
ISSN
1545-5955
Identifier
10.1109/TASE.2020.3038708
Publisher
Institute of Electrical and Electronics Engineers
Citation
ZHANG, Chen; LIM, Jun Long; LIU, Ouyang; MADAN, Aayush; ZHU, Yongwei; XIANG, Shili; WU, Kai; WONG, Rebecca Yen-Ni; PHUA, Jiliang Eugene; SABNANI, Karan M.; SIAH, Keng Boon; JIANG, Wenyu; WANG, Yixin; HAO, Emily Jianzhong; and HOI, Steven C. H..
A data-driven method for online monitoring tube wall thinning process in dynamic noisy environment. (2021). IEEE Transactions on Automation Science and Engineering. 19, (1), 441-456.
Available at: https://ink.library.smu.edu.sg/sis_research/6957
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/TASE.2020.3038708
Included in
Numerical Analysis and Scientific Computing Commons, Operations Research, Systems Engineering and Industrial Engineering Commons