{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:14:25Z","timestamp":1760145265196,"version":"build-2065373602"},"reference-count":42,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,27]],"date-time":"2024-06-27T00:00:00Z","timestamp":1719446400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51375354"],"award-info":[{"award-number":["51375354"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Pipelines are an important transportation form in industry. However, pipeline corrosion, particularly that occurring internally, poses a significant threat to safe operations. To detect the internal corrosion of a pipeline, a method utilizing piezoelectric sensors alongside singular spectrum analysis is proposed. Two piezoelectric patches are affixed to the exterior surface of the pipeline, serving the roles of an actuator and a sensor, respectively. During the detection, the signals excited by the actuator are transmitted through the pipeline\u2019s wall and are received by PZT2 through different paths, and the corresponding piezoelectric sensor captures the signals. Then, the response signals are denoised by singular spectrum analysis, and the first several wave packets in the response signals are selected to establish a feature for pipeline corrosion detection. At last, the envelope area of the selected packets is calculated as a feature to detect corrosion. To validate the proposed method, corrosion monitoring experiments are performed. The experimental results indicate that the envelope area of the first several wave packets from the response signals, following singular spectrum analysis, can serve as a feature to assess the degree of pipeline corrosion, and the index has a monotonic relationship with the corrosion depth of the pipeline. This method provides an effective way for pipeline corrosion monitoring.<\/jats:p>","DOI":"10.3390\/s24134192","type":"journal-article","created":{"date-parts":[[2024,6,27]],"date-time":"2024-06-27T11:19:02Z","timestamp":1719487142000},"page":"4192","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Piezoelectric Active Sensing-Based Pipeline Corrosion Monitoring Using Singular Spectrum Analysis"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8074-535X","authenticated-orcid":false,"given":"Dan","family":"Yang","sequence":"first","affiliation":[{"name":"Key Laboratory for Metallurgical Equipment and Control of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China"},{"name":"Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China"}]},{"given":"Hu","family":"Wang","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China"},{"name":"Precision Manufacturing Institute, Wuhan University of Science and Technology, Wuhan 430081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4591-0123","authenticated-orcid":false,"given":"Tao","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory for Metallurgical Equipment and Control of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China"},{"name":"Precision Manufacturing Institute, Wuhan University of Science and Technology, Wuhan 430081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6505-6916","authenticated-orcid":false,"given":"Guangtao","family":"Lu","sequence":"additional","affiliation":[{"name":"Key Laboratory for Metallurgical Equipment and Control of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China"},{"name":"Precision Manufacturing Institute, Wuhan University of Science and Technology, Wuhan 430081, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, J., Li, K., and Feng, L. (2022). Tracing the technological trajectory of coal slurry pipeline transportation technology: An HMM-based topic modeling approach. Front. Energy Res., 10.","DOI":"10.3389\/fenrg.2022.974747"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1177\/1475921719837718","article-title":"Inspection and monitoring systems subsea pipelines: A review paper","volume":"19","author":"Ho","year":"2020","journal-title":"Struct. Health Monit."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhu, J., Ren, L., Ho, S.-C., Jia, Z., and Song, G. (2017). Gas pipeline leakage detection based on PZT sensors. Smart Mater. Struct., 26.","DOI":"10.1088\/1361-665X\/26\/2\/025022"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yu, Y., Cheng, X., Wang, L., and Wang, C. (2022). Convolutional neural network-based quantitative evaluation for corrosion cracks in oil\/gas pipeline by millimeter-wave imaging. IEEE Trans. Instrum. Meas., 71.","DOI":"10.1109\/TIM.2022.3204992"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2828","DOI":"10.21595\/jve.2016.17040","article-title":"Damage detection of pipeline multiple cracks using piezoceramic transducers","volume":"18","author":"Du","year":"2016","journal-title":"J. Vibroeng."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Bastian, B.T., Jaspreeth, N., Ranjith, S.K., and Jiji, C. (2019). Visual inspection and characterization of external corrosion in pipelines using deep neural network. NDT E Int., 107.","DOI":"10.1016\/j.ndteint.2019.102134"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1016\/j.psep.2022.06.047","article-title":"Recurrent neural network-based model for estimating the life condition of a dry gas pipeline","volume":"164","author":"Shaik","year":"2022","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_8","unstructured":"Walker, J. (2010). In-Line Inspection of Pipelines Advanced Technologies for Economic and Safe Operation of Oil and Gas Pipelines, Verlag Moderne Industrie."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Beller, M., Barbian, A., and Strack, D. (2006, January 25\u201329). Combined in-line inspection of pipelines for metal loss and cracks. Proceedings of the International Pipeline Conference, Calgary, AB, Canada.","DOI":"10.1115\/IPC2006-10576"},{"key":"ref_10","unstructured":"Aron, J., Jia, J., Vance, B., Chang, W., Pohler, R., Gore, J., Eaton, S., Bowles, A., and Jarman, T. (2005). Development of an EMAT In-line Inspection System for Detection, Discrimination, and Grading of Stress Corrosion Cracking in Pipelines, Tuboscope Pipeline Services."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1016\/j.ijpvp.2006.07.010","article-title":"The use of radiography for thickness measurement and corrosion monitoring in pipes","volume":"83","author":"Edalati","year":"2006","journal-title":"Int. J. Press. Vessel. Pip."},{"key":"ref_12","unstructured":"Raude, A., Bouchard, M., and Sirois, M. (2018, January 15\u201319). Stress Corrosion Cracking Direct Assessment of Carbon Steel Pipeline Using Advanced Eddy Current Array Technology. Proceedings of the CORROSION 2018, Phoenix, AZ, USA."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1923","DOI":"10.1108\/EC-06-2019-0260","article-title":"Circumferential defect detection using ultrasonic guided waves: An efficient quantitative technique for pipeline inspection","volume":"37","author":"Da","year":"2020","journal-title":"Eng. Comput."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Lyu, F., Zhou, X., Ding, Z., Qiao, X., and Song, D. (2024). Application Research of Ultrasonic-Guided Wave Technology in Pipeline Corrosion Defect Detection: A Review. Coatings, 14.","DOI":"10.3390\/coatings14030358"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2609","DOI":"10.1177\/14759217221130939","article-title":"The use of circumferential guided waves to monitor axial cracks in pipes","volume":"22","author":"Rodgers","year":"2023","journal-title":"Struct. Health Monit."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1080\/10589759.2020.1839067","article-title":"Interaction of circumferential SH0 guided wave with circumferential cracks in pipelines","volume":"36","author":"Shi","year":"2021","journal-title":"Nondestruct. Test. Eval."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chen, J., Cao, L., and Song, G. (2023). Detection of the pipeline elbow erosion by percussion and deep learning. Mech. Syst. Signal Process., 200.","DOI":"10.1016\/j.ymssp.2023.110546"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kong, Q., Robert, R.H., Silva, P., and Mo, Y.L. (2016). Cyclic crack monitoring of a reinforced concrete column under simulated pseudo-dynamic loading using piezoceramic-based smart aggregates. Appl. Sci., 6.","DOI":"10.3390\/app6110341"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wang, F., Huo, L., and Song, G. (2017). A piezoelectric active sensing method for quantitative monitoring of bolt loosening using energy dissipation caused by tangential damping based on the fractal contact theory. Smart Mater. Struct., 27.","DOI":"10.1088\/1361-665X\/aa9a65"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, F., Chen, Z., and Song, G. (2021). Smart crawfish: A concept of underwater multi-bolt looseness identification using entropy-enhanced active sensing and ensemble learning. Mech. Syst. Signal Process., 149.","DOI":"10.1016\/j.ymssp.2020.107186"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yang, D., Zhang, X., Zhou, T., Wang, T., and Li, J. (2023). A Novel Pipeline Corrosion Monitoring Method Based on Piezoelectric Active Sensing and CNN. Sensors, 23.","DOI":"10.3390\/s23020855"},{"key":"ref_22","first-page":"734","article-title":"A multi-mode sensing system for corrosion detection using piezoelectric wafer active sensors","volume":"6932","author":"Yu","year":"2008","journal-title":"Sens. Smart Struct. Technol. Civ. Mech. Aerosp. Syst."},{"key":"ref_23","first-page":"941","article-title":"Study on the Electrochemical Anticorrosion Effect of Piezoelectric Materials in the Internal Environment of Water Supply Pipeline","volume":"20","author":"Li","year":"2021","journal-title":"Nat. Environ. Pollut. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Siswantoro, N., Do\u011fan, A., Priyanta, D., Zaman, M.B., and Semin, S. (2019). Possibility of piezoelectric sensor to monitor onshore pipeline in real time monitoring. Int. J. Mar. Eng. Innov. Res., 3.","DOI":"10.12962\/j25481479.v3i4.4951"},{"key":"ref_25","unstructured":"Yu, L., Giurgiutiu, V., Chao, Y., and Pollock, P. (November, January 30). In-situ multi-mode sensing with embedded piezoelectric wafer active sensors for critical pipeline health monitoring. Proceedings of the ASME International Mechanical Engineering Congress and Exposition, Columbus, OH, USA."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lu, P., Oetjen, K.A., Bender, D.E., Ruzinova, M.B., Fisher, D.A.C., Shim, K.G., Pachynski, R.K., Brennen, W.N., Oh, S.T., and Link, D.C. (2023). IMC-Denoise: A content aware denoising pipeline to enhance Imaging Mass Cytometry. Nat. Commun., 14.","DOI":"10.1038\/s41467-023-37123-6"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Tang, X., Liu, Y., Zheng, L., Ma, C., and Wang, H. (2009, January 4\u20135). Leak detection of water pipeline using wavelet transform method. Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology, Wuhan, China.","DOI":"10.1109\/ESIAT.2009.57"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Xu, Z.D., Zhu, C., and Shao, L.W. (2021). Damage identification of pipeline based on ultrasonic guided wave and wavelet denoising. J. Pipeline Syst. Eng. Pract., 12.","DOI":"10.1061\/(ASCE)PS.1949-1204.0000600"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Jin, Y., Xiao, H., and Fu, C. (2022, January 21\u201323). Power load forecasting based on SSA non-noise reduction processing. Proceedings of the 5th International Conference on Computer Science and Software Engineering, Guilin, China.","DOI":"10.1145\/3569966.3569999"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Luong, V.S., Le, M., Nguyen, K.D., Le, D.-K., and Lee, J. (2020). Electromagnetic Testing of Moisture Separation Reheater Tube based on Multivariate Singular Spectral Analysis. Appl. Sci., 10.","DOI":"10.3390\/app10113954"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Li, X., Guo, M., Zhang, R., and Chen, G. (2022). A data-driven prediction model for maximum pitting corrosion depth of subsea oil pipelines using SSA-LSTM approach. Ocean Eng., 261.","DOI":"10.1016\/j.oceaneng.2022.112062"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, J., Lu, G., Song, H., Wang, T., and Yang, D. (2023). Damage identification of thin plate-like structures combining improved singular spectrum analysis and multiscale cross-sample entropy (ISSA-MCSEn). Smart Mater. Struct., 32.","DOI":"10.1088\/1361-665X\/acb51a"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Golyandina, N., Korobeynikov, A., and Zhigljavsky, A. (2018). Singular Spectrum Analysis with R, Springer.","DOI":"10.1007\/978-3-662-57380-8"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.ymssp.2012.08.019","article-title":"Roller element bearing fault diagnosis using singular spectrum analysis","volume":"35","author":"Muruganatham","year":"2013","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_35","first-page":"1354","article-title":"Vibration-based damage monitoring in model plate-girder bridges under uncertain temperature conditions","volume":"29","author":"Park","year":"2006","journal-title":"Eng. Struct."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Yang, D., Xiong, M., Wang, T., and Lu, G. (2022). Percussion-based pipeline ponding detection using a convolutional neural network. Appl. Sci., 12.","DOI":"10.3390\/app12042127"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1007\/s13349-020-00457-6","article-title":"Monitoring of corrosion-induced damage to bolted joints using an active sensing method with piezoceramic transducers","volume":"11","author":"Cui","year":"2021","journal-title":"J. Civ. Struct. Health Monit."},{"key":"ref_38","unstructured":"Pourbaix, M. (2012). Lectures on Electrochemical Corrosion, Springer Science & Business Media."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"7958","DOI":"10.1109\/JSEN.2016.2600760","article-title":"Load monitoring of the pin-connected structure using time reversal technique and piezoceramic transducers-A feasibility study","volume":"16","author":"Liang","year":"2016","journal-title":"IEEE Sens. J."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1016\/j.asoc.2018.01.017","article-title":"A novel decomposition ensemble model for forecasting short term load time series with multiple seasonal patterns","volume":"65","author":"Zhang","year":"2018","journal-title":"Appl. Soft Comput. J."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1941","DOI":"10.1016\/j.ymssp.2005.07.002","article-title":"A new envelope algorithm of Hilbert\u2013Huang transform","volume":"20","author":"Qin","year":"2006","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_42","unstructured":"Ulrich, T. (2006). Envelope Calculation from the Hilbert Transform, Los Alamos National Laboratory. Technical Report."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/13\/4192\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:06:36Z","timestamp":1760108796000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/13\/4192"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,27]]},"references-count":42,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["s24134192"],"URL":"https:\/\/doi.org\/10.3390\/s24134192","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2024,6,27]]}}}