Anomaly detection of structural health monitoring data using the maximum likelihood estimation-based Bayesian dynamic linear model

Author:

Zhang Yi-Ming12,Wang Hao1ORCID,Wan Hua-Ping3ORCID,Mao Jian-Xiao1,Xu Yi-Chao45

Affiliation:

1. Key Laboratory of C&PC Structures of Ministry of Education, Southeast University, Nanjing, China

2. Department of Civil Engineering, Monash University, Clayton, VIC, Australia

3. College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, China

4. Jiangsu Transportation Research Institute Co., Ltd, Nanjing, China

5. State Key Laboratory of Safety and Health for In-Service Long Span Bridges, Nanjing, China

Abstract

Enormous data are continuously collected by the structural health monitoring system of civil infrastructures. The structural health monitoring data inevitably involve anomalies caused by sensors, transmission errors, or abnormal structural behaviors. It is important to identify the anomalies and find their origin (e.g. sensor fault or structural damage) to make correct interventions. Moreover, online anomaly identification of the structural health monitoring data is critical for timely structural condition assessment and decision-making. This study proposes an online approach for detecting anomalies of the structural health monitoring data based on the Bayesian dynamic linear model. In particular, Bayesian dynamic linear model, consisting of various components, is implemented to characterize the feature of real-time measurements. Expectation maximization algorithm and Kalman smoother are combined to estimate the Bayesian dynamic linear model parameters and generate log-likelihood functions. The subspace identification method is introduced to overcome the initialization issue of the expectation maximization algorithm. The log-likelihood difference of consecutive time steps is then used to determine thresholds without introducing extra anomaly detectors. The proposed Bayesian dynamic linear model-based approach is first illustrated by the simulation data and then applied to the structural health monitoring data collected from two long-span bridges. The results indicate that the proposed method exhibits good accuracy and high computational efficiency and also allows for reconstructing the strain measurements to replace anomalies.

Funder

National Natural Science Foundation of China

Jiangsu Provincial Key Research and Development Program

National Ten Thousand Talent Program for Young Top-notch Talents

China Scholarship Council

Scientific Research Foundation of Graduate School of Southeast University

Publisher

SAGE Publications

Subject

Mechanical Engineering,Biophysics

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