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| 基于多尺度时空窗主成分分析的结构智能损伤识别方法 |
| Structural Intelligent Damage Identification Method Based on Multi-scale Spatio-temporal Window Principal Component Analysis |
| 投稿时间：2024-04-10 修订日期：2024-05-31 |
| DOI： |
| 中文关键词: 结构健康监测 时空窗主成分分析 损伤识别 机器学习 |
| 英文关键词:Structural Health Monitoring Double-Window Principal Component Analysis Damage Identification Machine Learning |
| 基金项目: |
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| 中文摘要: |
| 时空窗主成分分析（Double-Window Principal Component Analysis，DWPCA）算法在传统PCA算法的基础上同时引入了时间窗和空间窗，通过移动的时间窗来有效区分不同的结构状态，通过空间窗剔除对损伤不敏感的数据。然而，在DWPCA算法中，时间窗口的尺寸是固定的，一般取为响应最大周期的1-2倍。考虑不同类型的损伤，如何确定时间窗尺度及其对损伤识别能力的影响仍有待探讨。本文首先将不同尺寸的时间窗（年/季/月）引入到DWPCA中，探究PCA特征在不同时间窗下随时间的演化规律；其次，将不同时间窗对应的PCA特征作为机器学习（Machine Learning，ML）输入，结合ML算法对结构进行损伤定位和量化任务；最后，使用双跨连续梁的仿真模型和Benchmark模型验证了方法的有效性。结果表明，以年为尺度的时间窗对损伤具有较好的鲁棒性，以月为尺度的时间窗对损伤具有较高的灵敏性；三种时间窗协同作用，能充分发挥各自的优势，进一步提高了ML的损伤定位和定量能力。 |
| 英文摘要: |
| The Double-Window Principal Component Analysis (DWPCA) algorithm introduces both temporal and spatial windows on the basis of traditional PCA algorithm, using a moving temporal window to effectively distinguish different structural states and employing a spatial window to eliminate data insensitive to damage. However, in the DWPCA algorithm, the size of the temporal window is fixed, typically chosen as 1-2 times the maximum period of response. The determination of the temporal window scale and its impact on damage recognition capability for different types of damage remains to be explored. This paper firstly introduces temporal windows of different scales (yearly/seasonal/monthly) into DWPCA to investigate the evolution patterns of PCA features over time under different temporal windows. Secondly, PCA features corresponding to different temporal windows are utilized as inputs for Machine Learning (ML) algorithms, combined with ML algorithms for structural damage localization and quantification tasks. Finally, the effectiveness of the method is validated using a simulation model of a double-span continuous beam and a Benchmark model. Results indicate that yearly temporal windows exhibit good robustness to damage, while monthly temporal windows demonstrate higher sensitivity to damage. The synergistic effect of the three types of temporal windows leverages their respective advantages, further enhancing the damage localization and quantification capabilities of ML. |
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