Pipeline running state prediction based on optical fiber sensing and optimized long short-term memory learning network

Xianyu Li; Xinglin Tong; Cui Zhang; Chong Xu; Bin Wei; ZhenMing Li

doi:10.1117/12.2682296

26 May 2023 Pipeline running state prediction based on optical fiber sensing and optimized long short-term memory learning network

Xianyu Li, Xinglin Tong, Cui Zhang, Chong Xu, Bin Wei, ZhenMing Li

Author Affiliations +

Proceedings Volume 12700, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023); 1270016 (2023) https://doi.org/10.1117/12.2682296
Event: International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 2023, Nanchang, China

Abstract

Pipeline plays an important role in petrochemical production, and its health monitoring is related to safety production. At present, the petrochemical production mainly relies on manual inspection to judge the pipeline status, which cannot achieve the full-time domain health monitoring. The purpose of this paper is to propose a method that uses a single weak fiber grating array as a sensor, extracts the vibration signal generated in the pipeline operation by the phase-sensitive optical time-domain reflection system, and improves the LSTM algorithm by using the variational mode decomposition principle and the arrangement entropy theory to predict the pipeline operation signal, so as to realize the abnormal state early warning. The experimental results show that the improved VMD-PE-LSTM model proposed in this paper has a very high accuracy in the prediction of pipeline vibration signals and provides a new direction for the early warning of abnormal pipeline operation.

Citation Download Citation

Xianyu Li, Xinglin Tong, Cui Zhang, Chong Xu, Bin Wei, and ZhenMing Li "Pipeline running state prediction based on optical fiber sensing and optimized long short-term memory learning network", Proc. SPIE 12700, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 1270016 (26 May 2023); https://doi.org/10.1117/12.2682296

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