Deep learning and data augmentation based data imputation for structural health monitoring system in multi-sensor damaged state

Abstract

Sensors, as an important part of structural health monitoring systems (SHMSs), will be abnormal sometimes due to their deterioration or environment effect, which will result in data loss during the health monitoring process of the structures. Data loss often happens in real monitoring applications, especially for wireless monitoring systems. Missing data, especially the long-term continuous missing data, will have a great impact on structural damage detection and condition evaluation. Usually, the long-term continuous missing data of the sensors are interpolated by traditional methods such as the correlation methods, which use a lot of normal monitoring data to build models and impute the missing data. However, in practice, many SHMSs in China have been in service for about 20 years or more, and many sensors installed have become faulty. It is usually difficult to obtain enough dataset fit for above methods. In this paper, a novel data imputation framework based on deep learning and data augmentation technique is therefore proposed, which enables the application of data modeling and missing data imputation based on the less remaining data when multiple sensors fail. Data imputation can be made between the same type of sensors (STSs) and also different types of sensors (DTSs). Generative adversarial network (GAN) based deep learning method and data augmentation technique are used for the imputation between the STSs; while long short-term memory (LSTM) network method is used for data imputation between the DTSs. The proposed methods are verified on the dataset of a real concrete bridge located in China, and results show that the proposed method achieves good performance.