Advanced thermal fluid leakage detection system with machine learning algorithm for pipe-in-pipe structure. (February 2023)

• Record Type: Journal Article
• Authors: Kim, Hayeol; Lee, Jewhan; Kim, Taekyeong; Park, Seong Jin; Kim, Hyungmo; Jung, Im Doo
• Is Part Of: Case studies in thermal engineering. Volume 42(2023)
• Publication Date: 2023-02
• Subjects: Pipe-in-pipe system -- High risk industry -- Leakage detection -- Distributed temperature sensing -- Machine learning; PIP pipe in pipe -- SFR sodium fast reactor -- DTS distributed temperature sensing -- ML machine learning -- CNN convolutional neural network; Heat engineering -- Case studies -- Periodicals; 621.40205
• DOI: 10.1016/j.csite.2023.102747
• Languages: English
• ISSNs: 2214-157X

Abstract

Abstract: Pipe-in-pipe (PIP) system is essential for high thermal and high pressure fluid transportation. However, in the existing PIP systems, fluid leakage between inner and outer pipe has been difficult to discover or detect, which has worked as bottle neck to utilize PIP system in high risk industries as nuclear reactor, chemical plant or oil drilling systems. Here, we propose a noble PIP leakage detection system utilizing distributed temperature sensing (DTS) with Machine Learning (ML). With the Fourier transformed spectrogram data from DTS, the ML assisted system was able to detect 0.2∼7 ml/min liquid leakage between inner and outer pipe with the accuracy of 91.67% with a single embedded optical fiber. Under varying operating temperature, the system successfully distinguished leakage and non-leakage states using the optimized convolutional neural network. Our developed PIP leakage detection system can be deployed in safety-critical industrial systems for autonomous leakage detection.