Due to external interference or fault, the collected sensor data
is often missed or abnormal. It’s significant to reconstruct the missing data, especially the large-scale missing data. In this paper, a missing sensor data reconstruction method based on
the adaptively updated dictionary is presented. The K-SVD algorithm is used to train the historical data frames which are collected at different time to generate the original dictionary atoms. Moreover, in order to meet the real-time, continuous characteristics of sensor data, an adaptive dictionary update algorithm is studied which . It calculates the correlation between the current reconstructed data frame and the largest weight frame in the training dictionary to update the dictionary. The experimental results are fully analyzed by the open data
set. The results show that the proposed method has higher
reconstructed precision especially the interval of data frames which is more than 60 minutes compared with other commonly used methods.