Sequence-To-Sequence Learning for Online Imputation of Sensory Data

Abstract

Online sensing can provide useful information in monitoring applications, for example, machine health monitoring, structural condition monitoring, environmental monitoring, and many more. Missing data is generally a significant issue in the sensory data that is collected online by sensing systems, which may affect the goals of monitoring programs. In this paper, a sequence-to-sequence learning model based on a recurrent neural network (RNN) architecture is presented. In the proposed method, multivariate time series of the monitored parameters is embedded into the neural network through layer-by-layer encoders where the hidden features of the inputs are adaptively extracted. Afterwards, predictions of the missing data are generated by network decoders, which are one-step-ahead predictive data sequences of the monitored parameters. The prediction performance of the proposed model is validated based on a real-world sensory dataset. The experimental results demonstrate the performance of the proposed RNN-encoder-decoder model with its capability in sequence-to-sequence learning for online imputation of sensory data.