Structural health monitoring data cleaning based on Bayesian robust tensor learning. (July 2023)

• Record Type: Journal Article
• Authors: Yi, Letian; Ding, Youliang; Hou, Jiale; Yue, Zixiang; Zhao, Hanwei
• Is Part Of: Structural health monitoring. Volume 22:Number 4(2023)
• Page Start: 2169
• Page End: 2192
• Publication Date: 2023-07
• Subjects: Robust -- matrix factorization -- Bayesian inference -- data cleaning -- structural health monitoring; Structural health monitoring -- Periodicals; Structural stability -- Periodicals; Strength of materials -- Periodicals; Nondestructive testing -- Periodicals; Constructions -- Stabilité -- Périodiques; Résistance des matériaux -- Périodiques; Contrôle non destructif -- Périodiques; Electronic journals; 624.17
• DOI: 10.1177/14759217221117238
• Languages: English
• ISSNs: 1475-9217

Abstract

Data cleaning is necessary to obtain high-quality monitoring data, which consists of two parts: anomaly identification and repair. A high-quality dataset is always used to repair data anomalies in current research, which may be difficult to obtain in practice because of the large number of data anomalies existing in real data. Besides, outliers, baseline shifts, and noise in dataset can lead to wrong missing restoration results. Therefore, the anomaly identification and location are needed to remove outliers, baseline shifts, and noise to regard the repair of these anomalies as missing data restoration. However, the identification of multiple data anomalies is time consuming and challenging. To this end, we propose a Bayesian robust tensor learning model to reconstruct the monitoring data tensor by extracting potential data features of the spatiotemporal tensor. A mixture of Laplace distribution with generalized inverse Gaussian distribution is used to model noise to enhance robustness. The tensor formed from the data containing anomalies can be input directly into the model without the identification and location of the data anomalies in advance. All data anomalies are repaired during the tensor reconstruction process. In addition, outliers, baseline shifts, and noise do not interfere with the missing restoration results owing to the robustness of the model. Furthermore, different cleaning strategies are proposed for dynamic and static response data. The data performance is verified using the real monitoring data of a concrete box girder bridge. The results show that the proposed method exhibits good data-cleaning performance when dealing with data containing a large number of data anomalies. In addition, as the abnormal rate increases, the proposed method still exhibits good data-cleaning performance.