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

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TASE.2020.3038708

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