Separation of the Temperature Effect on Structure Responses via LSTM—Particle Filter Method Considering Outlier from Remote Cloud Platforms

Abstract

Structural health monitoring (SHM) has been widely applied in the field of Mechanical and Civil Engineering in recent years. It is very hard to detect damage, however, using the measured data directly from the remote cloud platform of on-site structure, owing to changing environmental conditions. At the same time, outlier data from the remote cloud platform often occurs due to the harsh environmental conditions, interferences in the wireless medium, and the usage of low-quality sensors, which can greatly reduce the accuracy of structural health monitoring. In this paper, a novel temperature compensation method based on a long-short term memory (LSTM) network and the particle filter (PF) is proposed to separate the temperature effect from long-term structural health monitoring data. This method takes LSTMs as the state equation of PF, which solves the problem whereby PF cannot accurately derive the state equation for complex structures. A feedback model using the probability distribution generated by PF is developed to filter the observed value, thus measurement outliers can be successfully reduced. A numerical simulation and the measured deflection data from an SHM system are utilized to verify the proposed method. Results from the numerical simulation show that the LSTM-PF method can satisfactorily compensate for the temperature effect even when the nonlinear temperature effect is considered. Moreover, outputs from the SHM system of a large-scale suspension bridge indicate the temperature effect can be compensated and outliers can be appropriately reduced at the same time using the measured deflection data.

Keywords

• structural health monitoring
• temperature effect
• surrogate modeling
• outlier elimination
• remote cloud platform
• sensor