Missing Structural Health Monitoring Data Recovery Based on Bayesian Matrix Factorization

被引:3
|
作者
Sun, Shouwang [1 ]
Jiao, Sheng [1 ]
Hu, Qi [2 ]
Wang, Zhiwen [1 ]
Xia, Zili [3 ]
Ding, Youliang [4 ]
Yi, Letian [4 ]
机构
[1] YunJi Intelligent Engn Co Ltd, Shenzhen 518000, Peoples R China
[2] Zhongshan City Construct Grp Co Ltd, Zhongshan 528402, Peoples R China
[3] Hong Kong Zhuhai Macao Bridge Author, Zhuhai 519060, Peoples R China
[4] Southeast Univ, Key Lab C&PC Struct, Minist Educ, Nanjing 210096, Peoples R China
基金
中国国家自然科学基金;
关键词
matrix factorization; missing recovery; Bayesian inference; structural health monitoring; PREDICTION;
D O I
10.3390/su15042951
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The exposure of bridge health-monitoring systems to extreme conditions often results in missing data, which constrains the health monitoring system from working. Therefore, there is an urgent need for an efficient data cleaning method. With the development of big data and machine-learning techniques, several methods for missing-data recovery have emerged. However, optimization-based methods may experience overfitting and demand extensive tuning of parameters, and trained models may still have substantial errors when applied to unseen datasets. Furthermore, many methods can only process monitoring data from a single sensor at a time, so the spatiotemporal dependence among monitoring data from different sensors cannot be extracted to recover missing data. Monitoring data from multiple sensors can be organized in the form of matrix. Therefore, matrix factorization is an appropriate way to handle monitoring data. To this end, a hierarchical probabilistic model for matrix factorization is formulated under a fully Bayesian framework by incorporating a sparsity-inducing prior over spatiotemporal factors. The spatiotemporal dependence is modeled to reconstruct the monitoring data matrix to achieve the missing-data recovery. Through experiments using continuous monitoring data of an in-service bridge, the proposed method shows good performance of missing-data recovery. Furthermore, the effect of missing data on the preset rank of matrix is also investigated. The results show that the model can achieve higher accuracy of missing-data recovery with higher preset rank under the same case of missing data.
引用
收藏
页数:15
相关论文
共 50 条
  • [41] Recovery of Abnormal Data for Bridge Structural Health Monitoring Based on Deep Learning and Temporal Correlation
    Ju, Hanwen
    Deng, Yang
    Zhai, Wenqiang
    Li, Aiqun
    SENSORS AND MATERIALS, 2022, 34 (12) : 4491 - 4505
  • [42] A Performance Study of Random Interleaver based Data Loss Recovery Technique for Structural Health Monitoring
    Kanhere, Shubham
    Chouthankar, Krishna S.
    Hastey, Sibhali
    Thadikemalla, V. S. G.
    Gandhi, A. S.
    2018 9TH INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION AND NETWORKING TECHNOLOGIES (ICCCNT), 2018,
  • [43] Lost data neural semantic recovery framework for structural health monitoring based on deep learning
    Jiang, Kejie
    Han, Qiang
    Du, Xiuli
    COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, 2022, 37 (09) : 1160 - 1187
  • [44] A Bayesian Structural Equations Model for Multilevel Data with Missing Responses and Missing Covariates
    Das, Sonali
    Chen, Ming-Hui
    Kim, Sungduk
    Warren, Nicholas
    BAYESIAN ANALYSIS, 2008, 3 (01): : 197 - 224
  • [45] Incremental Bayesian matrix/tensor learning for structural monitoring data imputation and response forecasting
    Ren, Pu
    Chen, Xinyu
    Sun, Lijun
    Sun, Hao
    MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2021, 158
  • [46] HIERARCHICAL SPARSE BAYESIAN LEARNING FOR STRUCTURAL HEALTH MONITORING WITH INCOMPLETE MODAL DATA
    Huang, Yong
    Beck, James L.
    INTERNATIONAL JOURNAL FOR UNCERTAINTY QUANTIFICATION, 2015, 5 (02) : 139 - 169
  • [47] MATRIX FACTORIZATION FOR RECOVERY OF BIOLOGICAL PROCESSES FROM MICROARRAY DATA
    Kossenkov, Andrew V.
    Ochs, Michael F.
    METHODS IN ENZYMOLOGY: COMPUTER METHODS, PART B, 2009, 467 : 59 - 77
  • [48] Bayesian Modeling Approach for Forecast of Structural Stress Response Using Structural Health Monitoring Data
    Wan, Hua-Ping
    Ni, Yi-Qing
    JOURNAL OF STRUCTURAL ENGINEERING, 2018, 144 (09)
  • [49] Bayesian Kernelized Matrix Factorization for Spatiotemporal Traffic Data Imputation and Kriging
    Lei, Mengying
    Labbe, Aurelie
    Wu, Yuankai
    Sun, Lijun
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (10) : 18962 - 18974
  • [50] Bayesian model updating based on modal flexibility for structural health monitoring
    Feng, Z.
    Katafygiotis, L. S.
    EURODYN 2014: IX INTERNATIONAL CONFERENCE ON STRUCTURAL DYNAMICS, 2014, : 177 - 184