Missing-Data Imputation With Position-Encoding Denoising Autoencoders for Industrial Processes

Abstract

Missing values are a common occurrence in industrial datasets, resulting from the multiple sampling rates, sensor malfunctions, and transmission errors, whose presence can significantly affect the accuracy of data-driven models. An effective method to solve this problem is to impute the missing data in advance. This article proposes a new position-encoding denoising autoencoder (PE-DAE), which is motivated by the advantages of DAE in data reconstruction. To make use of the known information, the autocorrelation of the time-series data and the information on the missing positions are considered. Moreover, a self-paced learning (SPL) training strategy is proposed to improve the imputation performance under different levels of the missing data. The SPL training framework can first learn the knowledge structure of data with low missing rates and then gradually increase the difficulty, transitioning to learning more complex knowledge from data with higher missing rates. Finally, the proposed method is used for missing-data imputation in two real industrial processes. Comparative experiments show that the PE-DAE+SPL achieves the smallest error at all missing rates.