Iterative rolling difference-Z-score and machine learning imputation for wind turbine foundation monitoring
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| Publicado en: | PLoS One vol. 20, no. 9 (Sep 2025), p. e0331213 |
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| Otros Autores: | , , , , |
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Public Library of Science
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3247447238 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1932-6203 | ||
| 024 | 7 | |a 10.1371/journal.pone.0331213 |2 doi | |
| 035 | |a 3247447238 | ||
| 045 | 2 | |b d20250901 |b d20250930 | |
| 084 | |a 174835 |2 nlm | ||
| 100 | 1 | |a Li, Renjie | |
| 245 | 1 | |a Iterative rolling difference-Z-score and machine learning imputation for wind turbine foundation monitoring | |
| 260 | |b Public Library of Science |c Sep 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a In engineering structure performance monitoring, capturing real-time on-site data and conducting precise analysis are critical for assessing structural condition and safety. However, equipment instability and complex on-site environments often lead to data anomalies and gaps, hindering accurate performance evaluation. This study, conducted within a wind farm reinforcement project in Shandong Province, addresses these challenges by focusing on anomaly detection and data imputation for weld nail strain, anchor cable axial force, and concrete strain. We propose an innovative iterative rolling difference-Z-score method for anomaly detection and a machine learning-based imputation framework combining linear interpolation with LightGBM. Experimental results show that the iterative rolling difference-Z-score method detects single-point and clustered anomalies with a Z-score threshold of 4, achieving robust performance even with 80% data loss. The imputation framework maintains low mean squared error (MSE) of 0.0214–0.0227 and root mean squared error (RMSE) of 0.14–0.15 for continuous missing data scenarios (60–200 points), with reliable reconstruction up to 50% data loss. This research provides a robust solution for ensuring the precision and integrity of wind farm monitoring data, enhancing long-term structural reliability in renewable energy applications. | |
| 653 | |a Wind power | ||
| 653 | |a Accuracy | ||
| 653 | |a Performance evaluation | ||
| 653 | |a Wind farms | ||
| 653 | |a Concrete | ||
| 653 | |a Wind turbines | ||
| 653 | |a Machine learning | ||
| 653 | |a Standard scores | ||
| 653 | |a Monitoring | ||
| 653 | |a Learning algorithms | ||
| 653 | |a Strain gauges | ||
| 653 | |a Turbines | ||
| 653 | |a Data integrity | ||
| 653 | |a Renewable energy | ||
| 653 | |a Missing data | ||
| 653 | |a Data loss | ||
| 653 | |a Axial forces | ||
| 653 | |a Interpolation | ||
| 653 | |a Root-mean-square errors | ||
| 653 | |a Sensors | ||
| 653 | |a Neural networks | ||
| 653 | |a Onsite | ||
| 653 | |a Data collection | ||
| 653 | |a Engineering | ||
| 653 | |a Anomalies | ||
| 653 | |a Robustness (mathematics) | ||
| 653 | |a Structural reliability | ||
| 653 | |a Real time | ||
| 653 | |a Statistical methods | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Lu, Xiangxing | |
| 700 | 1 | |a Zhao, Jizhang | |
| 700 | 1 | |a Chen, Weibing | |
| 700 | 1 | |a Huanwei Wei | |
| 700 | 1 | |a Liu, Cong | |
| 773 | 0 | |t PLoS One |g vol. 20, no. 9 (Sep 2025), p. e0331213 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3247447238/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3247447238/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3247447238/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |