{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T09:16:53Z","timestamp":1778059013450,"version":"3.51.4"},"reference-count":29,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,15]],"date-time":"2024-09-15T00:00:00Z","timestamp":1726358400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["52075175"],"award-info":[{"award-number":["52075175"]}]},{"name":"National Natural Science Foundation of China","award":["52130511"],"award-info":[{"award-number":["52130511"]}]},{"name":"National Natural Science Foundation of China","award":["2023YFC3010500"],"award-info":[{"award-number":["2023YFC3010500"]}]},{"name":"National Key Research and Development Program of China","award":["52075175"],"award-info":[{"award-number":["52075175"]}]},{"name":"National Key Research and Development Program of China","award":["52130511"],"award-info":[{"award-number":["52130511"]}]},{"name":"National Key Research and Development Program of China","award":["2023YFC3010500"],"award-info":[{"award-number":["2023YFC3010500"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>As a critical component in industrial production, pipelines face the risk of failure due to long-term corrosion. In recent years, acoustic emission (AE) technology has demonstrated significant potential in online pipeline monitoring. However, the interference of flow-induced noise seriously hinders the application of acoustic emission technology in pipeline corrosion monitoring. Therefore, a pattern-recognition model for online pipeline AE monitoring signals based on blind source separation (BSS) and a convolutional neural network (CNN) is proposed. First, the singular spectrum analysis (SSA) was employed to transform the original AE signal into multiple observed signals. An independent component analysis (ICA) was then utilized to separate the source signals from the mixed signals. Subsequently, the Hilbert\u2013Huang transform (HHT) was applied to each source signal to obtain a joint time\u2013frequency domain map and to construct and compress it. Finally, the mapping relationship between the pipeline sources and AE signals was established based on the CNN for the precise identification of corrosion signals. The experimental data indicate that when the average amplitude of flow-induced noise signals is within three times that of corrosion signals, the separation of mixed signals is effective, and the overall recognition accuracy of the model exceeds 90%.<\/jats:p>","DOI":"10.3390\/s24185991","type":"journal-article","created":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T11:36:37Z","timestamp":1726486597000},"page":"5991","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Method for the Pattern Recognition of Acoustic Emission Signals Using Blind Source Separation and a CNN for Online Corrosion Monitoring in Pipelines with Interference from Flow-Induced Noise"],"prefix":"10.3390","volume":"24","author":[{"given":"Xueqin","family":"Wang","sequence":"first","affiliation":[{"name":"School of Safety Science and Engineering, Changzhou University, Changzhou 213164, China"}]},{"given":"Shilin","family":"Xu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Pressure Systems and Safety, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China"}]},{"given":"Ying","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Safety Science and Engineering, Changzhou University, Changzhou 213164, China"}]},{"given":"Yun","family":"Tu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Pressure Systems and Safety, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China"}]},{"given":"Mingguo","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Safety Science and Engineering, Changzhou University, Changzhou 213164, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gao, Y., Piltan, F., and Kim, J.-M. 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