{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T18:08:41Z","timestamp":1788804521878,"version":"build-2803163510"},"reference-count":9,"publisher":"L and H Scientific Publishing, LLC","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JVTSD"],"published-print":{"date-parts":[[2027,3,1]]},"abstract":"<jats:p>Flow-induced vibration (FIV) has traditionally been considered a source of mechanical failure and noise in pipeline systems. However, this study introduces a novel approach that leverages FIV as a diagnostic tool for non-intrusive flow rate estimation using vibration signals and machine learning (ML). A custom-built experimental test rig was developed, comprising a 2-meter PVC pipeline, a controlled centrifugal pump, and both single-axis and triaxial vibration sensors strategically placed along the pipe. This setup enables high-resolution vibration data acquisition under varying flow conditions without penetrating the pipe wall. The novelty lies in a non-intrusive approach and the application of supervised ML algorithms to decode flowrate from structural vibrations---an area that remains inadequately explored in the current literature. Initial polynomial regression analysis of single-axis sensor data revealed strong flow-vibration correlation (R^2&gt; 0.96), validating the physical basis for indirect flow measurement. Subsequently, six ML models---including Gradient Boosted Trees and Deep Learning---were trained on multiaxial vibration data, achieving high predictive performance with correlation coefficients up to 0.94 and RMSE as low as 1.81. In a two-class flowrate classification test (32 vs 25 mm^3\/s), all models demonstrated near-perfect accuracy. This work provides the first integrated experimental-ML framework for real-time, low-cost, and non-intrusive flow monitoring using FIV, offering significant potential for industrial applications where conventional flow meters are impractical.<\/jats:p>","DOI":"10.5890\/jvtsd.2027.03.002","type":"journal-article","created":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T17:41:02Z","timestamp":1788802862000},"page":"11-20","source":"Crossref","is-referenced-by-count":0,"title":["Flow Rate Estimation in Pipes using Flow-induced Vibration and Machine Learning"],"prefix":"10.5890","volume":"11","author":[{"given":"Musaab","family":"Zarog","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ibrahim","family":"Alnaabi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Said","family":"Almashrafi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed","family":"Al-Mamari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alkhatab","family":"AlNaamani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"7015","published-online":{"date-parts":[[2026,9,7]]},"reference":[{"key":"ref1","unstructured":"[1] Siba, M., Wanmahmood, W., Nuawi, M.Z., Rasani, R., and Nassir, M. (2016), Flow-induced vibration in pipes: challenges and solutions\u2014a review, Journal of Engineering Science and Technology, 11(3), 362-382."},{"key":"ref2","unstructured":"[2] Udoetok, E.S. (2018), Internal fluid flow induced vibration of pipes, Journal of Mechanical Design and Vibration, 6(1), 1-8."},{"key":"ref3","doi-asserted-by":"crossref","unstructured":"[3] Hafeez, A., Khushnood, S., Nizam, L.A., Usman, M., Rashid, M.M., Khan, H.F., and Qadir, A. (2023), Analysis of flow-induced vibrations in a heat exchanger tube bundle subjected to variable tube flow velocity, Advanced Science and Technology Research Journal, 17(2), 279-288.","DOI":"10.12913\/22998624\/161278"},{"key":"ref4","doi-asserted-by":"crossref","unstructured":"[4] Ying, S., Xiaohui, L., Chang, W., Wenan, Z., Jingsheng, L., Weisong, Z., and Guoyu, Z. (2012), Study of non-intrusion optical fiber fluid flow monitoring system using flow-induced pipe vibration, In 2012 International Conference on Industrial Control and Electronics Engineering, IEEE, 1284-1286.","DOI":"10.1109\/ICICEE.2012.341"},{"key":"ref5","unstructured":"[5] Kim, J., Park, S., and Lee, H. (2024), Deep neural network for predicting vibration power in pipeline systems, Journal of Mechanical Science and Technology, 38(2), 89-98."},{"key":"ref6","unstructured":"[6] Zhang, Y. and Lee, S. (2023), Using machine learning to estimate flow rates from pipe vibration, Sensors and Actuators A: Physical, 341, 113589."},{"key":"ref7","unstructured":"[7] Wang, L., Xu, T., and Chen, R. (2025), Distributed acoustic sensing for oil pipeline monitoring using flow-induced vibration, IEEE Sensors Journal, 25(1), 202-210."},{"key":"ref8","unstructured":"[8] Al-Zakwani, A., Rahman, A.A., and Salim, M. (2025), Smart monitoring in pipelines: a review of hybrid sensing and machine learning, International Journal of Pressure Vessels and Piping, 200, 104901."},{"key":"ref9","doi-asserted-by":"crossref","unstructured":"[9] Li, W., Zhang, D., and Shi, X. (2023), Establishment of a flow-induced vibration power database based on deep neural network machine learning method, Ocean Engineering, 285, 115463.","DOI":"10.1016\/j.oceaneng.2023.115463"}],"container-title":["Journal of Vibration Testing and System Dynamics"],"original-title":[],"language":"en","deposited":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T17:44:33Z","timestamp":1788803073000},"score":1,"resource":{"primary":{"URL":"https:\/\/lhscientificpublishing.com\/index.php\/jvtsd\/article\/view\/2514"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,7]]},"references-count":9,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2027,3,1]]}},"URL":"https:\/\/doi.org\/10.5890\/jvtsd.2027.03.002","relation":{},"ISSN":["2475-4811","2475-482X"],"issn-type":[{"value":"2475-4811","type":"print"},{"value":"2475-482X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,7]]}}}