Zhang, Chen Lim, Jun Long Liu, Ouyang Madan, Aayush Zhu, Yongwei Xiang, Shili Wu, Kai Wong, Rebecca Yen-Ni Eugene Jiliang, Phua Sabnani, Karan M. Siah, Keng Boon Jiang, Wenyu Wang, Yixin Hao, Emily Jianzhong Hoi, Steven C. H., "A Data-Driven Method for Online Monitoring Tube Wall Thinning Process in Dynamic Noisy Environment," IEEE Transactions on Automation Science and Engineering, Jan. 2021.
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.
This research / project is supported by the National Research Foundation, Singapore - Energy Innovation Research Programme, under Sembcorp-EMA Energy Technology Partnership
Grant Reference no. : NRF2015EWT-EIRP001-010
This research / project is supported by the National Natural Science Foundation of China - Grant
Grant Reference no. : 71901131, 71932006
This research / project is supported by the Tsinghua University - Intelligent Logistics and Supply Chain Research Center
Grant Reference no. : THUCSL20182911756-001