Early Anomaly Pre-Warning of Buried Pipelines via Dynamic Acceleration Signals: An ICEEMDAN-LSTM Framework.
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| Title: | Early Anomaly Pre-Warning of Buried Pipelines via Dynamic Acceleration Signals: An ICEEMDAN-LSTM Framework. |
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| Authors: | Guo YQ; College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China., Zhu ZX; College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China., Xia ZH; China-Pakistan Belt and Road Joint Laboratory on Smart Disaster Prevention of Major Infrastructures, Southeast University, Nanjing 210096, China., Zang XL; China-Pakistan Belt and Road Joint Laboratory on Smart Disaster Prevention of Major Infrastructures, Southeast University, Nanjing 210096, China., Li JB; Jiangsu Southeast Special Engineering & Technology Co., Ltd., Nanjing 210009, China. |
| Source: | Sensors (Basel, Switzerland) [Sensors (Basel)] 2026 May 30; Vol. 26 (11). Date of Electronic Publication: 2026 May 30. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: MEDLINE; PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1424-8220 |
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| DOI: | 10.3390/s26113463 |
