江西水利电力大学学报2026，Vol.45Issue(1)：15-22,8.
基于KPCA-ICEEMDAN-IWT的特高拱坝变形监测数据预处理方法
A data preprocessing method for deformation monitoring of extra-high arch dams based on KPCA-ICEEMDAN-IWT
摘要
Abstract
To address the challenges of multicollinearity and complex noise interference in deformation monitoring data for ultra-high arch dams,this study proposes a joint preprocessing method integrating hybrid kernel principal component analy-sis(KPCA),improved adaptive noise-complete set empirical mode decomposition(ICEEMDAN),and improved wavelet thresholding(IWT).This method first constructs a comprehensive deformation index via Bayesian optimization of mixed-ker-nel KPCA to eliminate redundant information and collinearity effects.Subsequently,ICEEMDAN is employed for adaptive signal decomposition,enabling multi-criteria identification of high-frequency noise components.Finally,IWT filters out noise components and reconstructs the effective signal.Our case studies demonstrate that this combined model outperforms standa-lone ICEEMDAN,WT,and IWT methods in metrics such as root mean square error and correlation coefficient.It significant-ly enhances noise removal accuracy while effectively preserving useful high-frequency information,providing a more reliable data preprocessing technique for safety monitoring of ultra-high arch dams.关键词
变形监测/核主成分分析/ICEEMDAN/小波阈值/异常值识别/数据降噪Key words
deformation monitoring/kernel principal component analysis/ICEEMDAN/wavelet thresholding/outlier detec-tion/data noise reduction分类
建筑与水利引用本文复制引用
牛景太,余春燕,吴邦彬..基于KPCA-ICEEMDAN-IWT的特高拱坝变形监测数据预处理方法[J].江西水利电力大学学报,2026,45(1):15-22,8.基金项目
国家自然科学基金项目(52579124) （52579124）
江西省教育科学技术研究项目(GJJ2201501) （GJJ2201501）
江西省水利厅科技项目(202527ZDKT16) （202527ZDKT16）
