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

• 作者: Kaitai TONG Teng LI
• 来源期刊: 仪器仪表学报:英文版
• 学科: 工学
• 关键词: DATA IMPUTATION RECURRENT NEURAL Network Sequence-To-Sequence Learning SEQUENCE Prediction
• 年，卷（期）: 2019,（2）
• 页码范围: 63-70
• 页数: 8页
• 分类号: TP212

摘要

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.