Affiliation:
1. School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Abstract
It is important for data modeling to comply with a data observation window of physical variables behind the data. In this paper, a multivariate data alignment method is proposed to follow different time scales and different role effects. First, the length of the sliding windows is determined by the frequency characteristics of the time-series reconstruction. Then, the time series is aligned to the length of the window by a sequence-to-sequence neural network. This neural network is trained by replacing the loss function with dynamic time warping (DTW) in order to prevent the losses of the time series. Finally, the attention mechanism is introduced to adjust the effect of different variables, which ensures that the data model of the matrix is in accord with the intrinsic relation of the actual system. The effectiveness of the approach is demonstrated and validated by the Tennessee Eastman (TE) model.
Subject
Process Chemistry and Technology,Chemical Engineering (miscellaneous),Bioengineering
Reference31 articles.
1. Gao, Z., Chen, M.Z.Q., and Zhang, D. (2021). Special Issue on “Advances in condition monitoring, optimization and control for complex industrial processes”. Processes, 9.
2. Overview of surrogate modeling in chemical process engineering;McBride;Chem. Ing. Tech.,2019
3. Multiphysics modelling of manufacturing processes: A review;Jabbari;Adv. Mech. Eng.,2018
4. Applications of hybrid models in chemical, petroleum, and energy systems: A systematic review;Zendehboudi;Appl. Energy,2018
5. Assembly process modeling mechanism based on the product hierarchy;Liu;Int. J. Adv. Manuf. Technol.,2016